Download Neuronal control of leech behavior - Emory Biology

Progress in Neurobiology 76 (2005) 279–327
www.elsevier.com/locate/pneurobio
Neuronal control of leech behavior
William B. Kristan Jr.a, Ronald L. Calabrese b, W. Otto Friesen c,*
a
Section of Neurobiology, Division of Biological Sciences, 9500 Gilman Dr., University of California, San Diego,
La Jolla, CA 92093-0357, USA
b
Department of Biology, Emory University, 1510 Clifton Road, Atlanta, GA 30322, USA
c
Department of Biology, Gilmer Hall, University of Virginia, P.O. Box 400328,
Charlottesville, VA 22904-4328, USA
Received 7 April 2005; received in revised form 23 August 2005; accepted 26 September 2005
Abstract
The medicinal leech has served as an important experimental preparation for neuroscience research since the late 19th century. Initial
anatomical and developmental studies dating back more than 100 years ago were followed by behavioral and electrophysiological investigations in
the first half of the 20th century. More recently, intense studies of the neuronal mechanisms underlying leech movements have resulted in detailed
descriptions of six behaviors described in this review; namely, heartbeat, local bending, shortening, swimming, crawling, and feeding.
Neuroethological studies in leeches are particularly tractable because the CNS is distributed and metameric, with only 400 identifiable, mostly
paired neurons in segmental ganglia. An interesting, yet limited, set of discrete movements allows students of leech behavior not only to describe
the underlying neuronal circuits, but also interactions among circuits and behaviors. This review provides descriptions of six behaviors including
their origins within neuronal circuits, their modification by feedback loops and neuromodulators, and interactions between circuits underlying with
these behaviors.
# 2005 Elsevier Ltd. All rights reserved.
Keywords: Elemental oscillators; Interneurons; Serotonin
Contents
1.
2.
3.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1. Anatomy and electrophysiology . . . . . . . . . . . . . . . . . . . . . . .
1.2. The hydroskeleton and behaviors . . . . . . . . . . . . . . . . . . . . . .
Circulation and heartbeat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1. The heartbeat neural control system of the leech . . . . . . . . . . .
2.2. The elemental oscillators . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3. Mechanisms of oscillation in an elemental half-center oscillator
2.4. Coordination in the beat timing network . . . . . . . . . . . . . . . . .
2.5. Heartbeat motor pattern switching by switch interneurons . . . . .
2.6. Gaps in our current knowledge . . . . . . . . . . . . . . . . . . . . . . .
Overt behaviors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2. Local bending . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.1. Motor neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.2. Mechanosensory neurons that produce local bending . .
3.2.3. Local bend interneurons . . . . . . . . . . . . . . . . . . . . . .
3.2.4. The local bend response as a directed behavior . . . . . .
3.2.5. Gaps in our current knowledge . . . . . . . . . . . . . . . . .
* Corresponding author. Tel.: +1 434 982 5493; fax: +1 434 982 5626.
E-mail address: [email protected] (W.O. Friesen).
0301-0082/$ – see front matter # 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.pneurobio.2005.09.004
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W.B. Kristan Jr. et al. / Progress in Neurobiology 76 (2005) 279–327
3.3.
4.
Shortening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.1. Whole-body shortening . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.2. Local shortening. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.3. Gaps in our knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4. Swimming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.1. History: reflex chain versus central pattern generator . . . . . . .
3.4.2. Swimming behavior and motor control . . . . . . . . . . . . . . . . .
3.4.3. Central oscillator circuits. . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.4. Control of swimming activity . . . . . . . . . . . . . . . . . . . . . . .
3.4.5. Neuromodulatory control: serotonin and other biogenic amines
3.4.6. Role of sensory feedback . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.7. Functional aspects of the central oscillator . . . . . . . . . . . . . .
3.4.8. Gaps in our current knowledge . . . . . . . . . . . . . . . . . . . . . .
3.5. Vermiform crawling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.1. Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.2. Kinematics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.3. Motor neuron activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.4. Sensory input. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.5. Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.6. Initiation of crawling . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.7. Gaps in our current knowledge . . . . . . . . . . . . . . . . . . . . . .
3.6. Feeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.6.1. Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.6.2. Chemosensation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.6.3. Motor patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.6.4. Regulation and plasticity . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.6.5. Gaps in our knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.7. Interactions among behaviors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.8. Methodologies and approaches for further research . . . . . . . . . . . . . .
3.8.1. Functional indicator dyes . . . . . . . . . . . . . . . . . . . . . . . . . .
3.8.2. Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.8.3. Plasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.8.4. Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction
The major goal of neurobiology is to understand how
the brain works: how it senses the external world and internal
states, how it processes this sensory input, how it evaluates
different inputs to select an appropriate motor act, and how it
generates that behavior. One approach to studying these
questions is to study the function of a particular neural
structure (e.g. the superior colliculus or the habenular
nucleus) in a complex brain and ask how it works. Another
approach is to select a behavior and ask how the properties
of neurons and their interconnections produce that behavior.
The latter approach is the more direct, but is possible only
in animals with relatively simple nervous systems, or in
selected parts of complex nervous systems with neurons that
are identifiable from animal to animal. For example,
behavioral circuits have been described in a number of such
animals: mollusks (Arshavsky et al., 1998; Satterlie et al.,
2000; Brembs et al., 2002; Dembrow et al., 2003; Sakurai
and Katz, 2003; Jing and Gillette, 2003; Staras et al., 1999;
Bristol et al., 2004), crustaceans (Selverston et al., 2000;
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Teshiba et al., 2001; Prinz et al., 2003; Beenhakker et al.,
2004), insects (Sasaki and Burrows, 2003; Wang et al., 2003;
Riley et al., 2003; Wilson et al., 2004; Daly et al., 2004),
amphibians (Roberts et al., 1999; Combes et al., 2004), fish
(Higashijima et al., 2003; Grillner, 2003), and rodents
(Sekirnjak et al., 2003; Kiehn and Butt, 2003; Yvert et al.,
2004).
Rhythmic movements such as chewing, respiratory
movements, locomotory movements, and, in some animals,
heartbeat are of particular interest because of their
combination of complex dynamics and relative stereotypy
(Marder and Calabrese, 1996; Stein et al., 1997; Orlovsky
et al., 1999). Oscillatory networks of central neurons
are important components of most such motor patterngenerating networks. The anatomical wiring and synaptic
connectivity within a network is the backbone on which
intrinsic and synaptic properties of component neurons
operate to produce network dynamics. The states of these
intrinsic and synaptic properties are themselves dynamic,
being subject to modulation through a multiplicity of
sensory inputs provided by neurons extrinsic and intrinsic
W.B. Kristan Jr. et al. / Progress in Neurobiology 76 (2005) 279–327
281
to the network (Katz, 1995; Harris-Warrick et al., 1997;
Nusbaum et al., 1997).
A very useful animal for establishing the neuronal bases of
behaviors has been the leech, particularly the European
medicinal leech, Hirudo medicinalis. In this animal, more
behaviors have been studied in neuronal terms than in any other.
This review provides an overview of all the behaviors that have
been studied, and an update of less comprehensive but more
detailed reviews that have appeared elsewhere (Brodfuehrer
et al., 1995b; Calabrese et al., 1995; Kristan et al., 1995). There is
always the possibility that the neuronal mechanisms found in a
particular animal will be unique to that animal, due to its specific
evolutionary history and individual peculiarities of anatomy and
biomechanics. We, however, believe the opposite: that there are
general strategies for producing behaviors that will be found in
all animals with a central nervous system (CNS).
There are many technical reasons why the medicinal leech is
an auspicious animal for identifying behaviorally relevant
neuronal systems. Some of the reasons are generally true of
simple animals, and others are true of the leech in particular. It
is worth enumerating the list to indicate why the medicinal
leech has been so useful in studying the neuronal bases of
behaviors:
1. The leech nervous system is relatively simple (Fig. 1A) and
readily accessible even while the animal is behaving in a
variety of semi-intact preparations (Fig. 1B), making it
possible to relate motor patterns directly to behaviors.
2. Quite accurate representations of all the behaviors, or at least
their rudiments, can be elicited in isolated nerve cords
(Fig. 1B), where intracellular and optical recording is more
favorable.
3. The neurons are easily seen and readily identified, based on
the location of their somata (Fig. 1C), morphology (Fig. 1D),
and physiological properties.
4. Intracellular neuronal activity can be recorded readily
because the somata are relatively large (10–80 mm) and
every soma is visible in segmental ganglia. These properties
also make optical recording feasible.
5. Long, easily accessible peripheral nerves allow for stimulation of selected neurons and monitoring of neuronal activity
with extracellular electrodes.
6. Most relevant electrical parameters can be measured.
Intracellular recordings from somata reveal relatively large,
individual synaptic potentials, which are not greatly
attenuated from their origins in the neuropil, and attenuated
action potentials.
7. The nervous system is iterated, with homologous neurons
found in most, if not all, 21 segmental ganglia (Fig. 1). So
despite having more than 10,000 neurons, the functional unit
(i.e. the number of different kinds of neurons) of the leech
CNS is relatively small. For instance, there are only 400
neurons per segmental ganglion (Macagno, 1980), and most
of these are paired. Thus, in essence, the segmental nerve
cord (roughly corresponding to the spinal cord in vertebrates) consists of 42 copies (one on each side of 21
segments) of a basic unit of 200 neurons.
Fig. 1. Anatomy of the medicinal leech and its nervous system. (A) Schematic diagram of the leech, showing the major features of its nervous system.
There are 21 segmentally homologous midbody ganglia, numbered M1–M21.
The anterior brain (inset) consists of a supraesophageal ganglion (sup.) that is
part of the prostomium, plus a subesophageal ganglion (sub.), which forms
from the coalescence of the four most anterior embryonic ganglia that are
visible in the adult brain as neuromeres 1–4. (B) Types of preparations used to
study the neuronal bases of leech behaviors. The kinematics of all behaviors
have been characterized in intact animals (top panel) by measuring the
distances between markers placed on the external body wall in successive
frames of a movie or video. A variety of semi-intact preparations (example in
middle panel) have provided intracellular and extracellular recordings during
each of the behaviors, thereby revealing the underlying motor neuronal firing
patterns. The isolated nervous system (bottom panel), in its entirety or in
pieces, produces motor patterns that are distinguishable as the neuronal
substrates of each of the behaviors. Such preparations are the most useful for
electrophysiological characterizations of neuronal properties and synaptic
connections. (C) Schematic view of the ventral surface of a midbody ganglion, indicating the arbitrary numbering scheme used to identify ganglionic
neurons. Most midbody ganglia have the same neurons and locations of the
soma. The dotted lines indicate the packet margins formed by the six giant
glial cells, each of which encapsulates characteristic clusters of neuronal
somata. The scale bar (200 mm) indicates the size of a midbody ganglion in a
mature leech weighing 2–5 g. The functions of about a third of the neurons
are known. (D) Structure of a single neuron. Dye, injected into the soma,
diffused into the processes in the center of the ganglion, where all synaptic
contacts are made. This region is termed the neuropil. This neuron, cell 208,
has extensive, bilaterally symmetric branches and sends a single axon
posteriorly down one of the two lateral connectives (the small, medial
connective is called ‘‘Faivre’s nerve’’). Based upon the location of its soma
and its branching pattern, each neuron has a distinctive morphology. The
scale bar in D represents 100 mm.
282
W.B. Kristan Jr. et al. / Progress in Neurobiology 76 (2005) 279–327
8. Most neurons in the CNS are unique rather than members of
functionally identical clusters, hence activating or ablating
single neurons (irreversibly by killing or reversibly by
hyperpolarization) often has behaviorally detectable consequences.
Because of these favorable features, it is possible, in
principle, to identify every neuron that contributes substantially
to any leech behavior. In practice, this task is far from trivial, so
that no single behavior has yet been completely characterized.
In no other system, however, have so many behaviors been
investigated and described at the neuronal circuit level. This
review is intended to provide a brief overview of the state of
what is known, and what remains to be known about the most
completely described behaviors. To understand these descriptions, background information concerning basic leech anatomy
and electrophysiology is essential. After this introduction, the
neuronal circuits underlying six different behaviors are
discussed individually, followed by a discussion of how these
circuits interact. A final section provides a vision of how the
leech may prove useful for future research.
1.1. Anatomy and electrophysiology
Leeches are annelids, all of which are segmented worms
(Fig. 1A). Unlike most other annelids, leeches have a fixed
number of segments – 32 – plus an anterior non-segmental
region called the prostomium. The segments form as a repeated
iteration of divisions of the same stem cells, whereas the
prostomium is derived from a different set of stem cells (Stent
et al., 1992). The prostomium and the most anterior four
segments form the head and the most posterior seven segments
form the tail. There are a variety of specializations in the head
and tail, the most striking of which are the suckers. The mouth
is in the middle of the front sucker, whereas the anus is located
in the body wall anterior and dorsal to the posterior sucker. At
rest, the posterior sucker is usually attached to the substrate.
The anterior end is used to explore the environment, so that the
anterior sucker is typically attached only when the leech is
crawling or feeding. The body is a tube formed by epidermis
and muscles, which encases the internal organs: the gut and
intestines, the nephridia and urinary sacs, the reproductive
organs, and the blood vessels (Fig. 2A). The circulatory system
of a leech is closed, with four major longitudinal blood vessels
that run the length of the leech and a mesh of circumferentially
directed vessels connecting them. The dorsal and ventral
longitudinal vessels are passive (they function as veins) and the
lateral tubes are contractile (they function as hearts).
The leech CNS consists of a ventral nerve cord with a brain
at each end (Fig. 1A). Each segment contains a single ganglion,
which communicates with the adjacent anterior and posterior
ganglia via three connectives (a pair of large lateral connectives
and a smaller medial connective, known as Faivre’s Nerve). The
four anterior ganglia fuse during embryogenesis to form a
subesophageal ganglion, and a supraesophageal ganglion forms
within the prostomium. The borders of these individual ganglia
are visible in the adult. The neuronal compartments of the four
ganglia are called neuromeres. Together, the supraesophageal
and subesophageal ganglia form the anterior brain (sometimes
called the head brain). Similarly, the last seven ganglia in the
chain fuse embryonically to form the posterior brain (also
called the tail brain). The neuromeres in the anterior brain
are denoted as R1–4 (rostral neuromeres 1–4), those in the
posterior brain are C1–7 (caudal neuromeres 1–7), and the
individual, mid-body ganglia are labeled M1–M21.
Neuronal somata within the CNS are roughly spherical, and
are located on the surface of segmental ganglia and terminal
brains. In midbody ganglia, somata are in 10 clusters – four on
the dorsal surface and six on the ventral surface – delineated by
giant glial cells that effectively engulf the somata of dozens of
neurons. In fact, modern-day characterization of the function of
leech neurons began with a series of elegant studies by Stephen
Kuffler and his colleagues using these giant glial cells to study
the electrical properties and potential functions of glia (Kuffler
and Potter, 1964; Nicholls and Kuffler, 1964). They concluded
that the membranes of these glial cells are nearly perfect K+
electrodes, and that their contributions to the electrical function
of the nervous system is to sequester K+ ions released by active
neurons in order to buffer the effects of local release of the K+.
These giant glial cells, therefore, gather the neuronal somata
into packets, with the lateral edges serving as packet margins
that provide useful markers for identifying neurons. The ventral
surface of a typical midbody ganglion is shown in Fig. 1C. Most
or all of the leech central neurons are identifiable from animalto-animal and segment-to-segment on the bases of the size and
location of their somata within a cluster, as well as their
characteristic electrophysiological properties and morphological features (Muller et al., 1981).
All neurons in the leech CNS are monopolar: a single
process extends from each soma. Typically, this process gives
rise to one or more axons that leave the ganglion, via nerves to
the periphery in the case of sensory and motor neurons (MNs),
and via the connectives in the case of interneurons (INs) and
some sensory and secretory neurons. Secondary branches
emerge from the main process; these side branches may
subdivide to generate many orders of branching (Fig. 1D).
Synaptic connections are made primarily on these fine
branches.
All the ganglionic MNs that send axons via segmental nerves
to muscles in the body wall (Stuart, 1970; Ort et al., 1974;
Norris and Calabrese, 1987) and to the lateral heart tubes
(Thompson and Stent, 1976a) are located within the CNS.
The muscles used by the leech to make overt movements are of
four types: longitudinal, circular, oblique, and dorsoventral
(Fig. 2B). Contractions of each of these muscles produce
characteristic types of movements: longitudinal muscle
contractions produce shortening, circular muscle contractions
produce a reduction in cross-section and elongation, oblique
muscle contractions cause stiffening at an intermediate body
length, and dorsoventral muscle contractions cause a flattening
of the body and contribute to elongation. Each MN connects to
a single muscle type, and only to muscle fibers on either the left
or right side of its own segment, and then only to a regional
subset of muscle fibers. For instance, MNs – both excitatory and
W.B. Kristan Jr. et al. / Progress in Neurobiology 76 (2005) 279–327
283
Fig. 2. Schematic views of a leech midbody segment. (A) Cut-away view of the middle of a leech, showing the location of the central nervous system, major
peripheral nerves, blood vessels, viscera, and musculature. Each midbody ganglion connects to adjacent ganglia via connectives and to the periphery through
characteristic nerves. The four major longitudinal blood vessels (heart tubes) connect to one another via circumferential blood vessels. The lateral longitudinal blood
vessels are contractile and serve as hearts. The ventral nerve cord is suspended in the ventral blood vessel, which, like the dorsal vessel, is passive. A rich capillary
vascularization of the skin provides for gas exchange in these aquatic animals, so that the skin effectively functions as a gill. (B) A simplified schematic diagram,
emphasizing the geometric relationships of the muscle groups used to produce the behaviors shown in Fig. 3. Contractions of circular muscles produce elongation,
longitudinal muscles produce shortening, and dorsoventral (DV) muscles produce flattening. Oblique muscles stiffen the animal at a length intermediate between
maximal contraction and maximal elongation. Not shown are annulus erector muscles, located in the skin, that cause the individual annuli (five per segment in the
midbody) to form peaked ridges around the animal.
inhibitory – that project to longitudinal muscles, innervate either
dorsal, dorsolateral, lateral, ventrolateral, ventral, or dorsolateroventral regions (Stuart, 1970). The excitatory neuromuscular
transmitter is ACh (Sargent, 1977) and the inhibitory transmitter
is GABA (Cline, 1986). Activity of various combinations of
these MNs in different temporal patterns produce the behaviors
described in subsequent sections.
Leeches have a variety of sensory receptors. For instance,
there are light-sensitive receptors in the sensilla located in each
segment and in the eyes (a pair of expanded sensilla located on
the lateral edge of each of the first five segments) (Kretz et al.,
1976); chemoreceptors, in placodes on the upper lip
(Elliott, 1987); stretch receptors, embedded in the dorsal, lateral,
and ventral body wall of each segment (Blackshaw et al., 1982;
Blackshaw and Thompson, 1988; Blackshaw, 1993; Cang et al.,
2001); and mechanoreceptors of different sorts. There are
ciliated mechano-receptive neurons whose somata are in the
sensilla (DeRosa and Friesen, 1981; Phillips and Friesen, 1982)
and respond to movements of the water (Brodfuehrer and
Friesen, 1984). There are also mechanoreceptors with somata in
the CNS (Nicholls and Baylor, 1968) that have free nerve endings
in the skin (Blackshaw, 1981) and respond to different intensities
of stimulation to the skin: light touch (T cells), pressure (P cells),
and noxious stimuli (N cells). These neurons have primary
284
W.B. Kristan Jr. et al. / Progress in Neurobiology 76 (2005) 279–327
receptive fields within their own segment, and secondary ones
(via axons through connectives) in the adjacent ganglia both
anterior and posterior. There are three pairs of T cells and two
pairs of P cells. The receptive fields of these cells divide up the
body wall into roughly equal, overlapping receptive fields around
the circumference of the animal. There are two pairs of N cells in
each ganglion, each of which innervates half the ipsilateral body
wall. All the N cells respond to noxious mechanical stimuli,
but the two on each side differ in their responses to other stimuli,
such as heat and low pH (Pastor et al., 1996).
The great majority of the neuronal somata within the leech
CNS, as in other animals, are neither sensory nor MNs; rather
they are INs without direct connection to the periphery. These
INs were identified largely by methodically searching for
neurons associated with specific behaviors. For example,
specific neurons were identified when intracellular current
injection evoked (or terminated) a behavior in either semi-intact
preparations, or fictive behavior in the isolated CNS. Using this
technique, INs were found that participate in seven behaviors:
heartbeat (Thompson and Stent, 1976b), local bend (Lockery
and Kristan, 1990b), shortening (Shaw and Kristan, 1995),
swimming (Friesen et al., 1978; Weeks, 1982a,b,c; Friesen,
1985, 1989b; Brodfuehrer and Friesen, 1986a,b,e), crawling
(Eisenhart et al., 2000), reproduction (Zipser, 1979) and
feeding (Zhang et al., 2000). It is largely true that homologs of
each of the neurons found in one ganglion can be found in the
remaining 20 segments. There are exceptions to this general
rule, which are pointed out in the sections below. In addition,
homologs of the Retzius neurons (Lent, 1977) and several
mechanosensory neurons (Yau, 1976) have been found in the
neuromeres of the subesophageal ganglion, although many of
the INs in the subesophageal ganglion do not appear to have
homologs in the segmental ganglia (Brodfuehrer and Friesen,
1986a,b,c,e). This ability to identify a particular neuron in
segment after segment and in animal after animal has greatly
aided the characterization of neuronal circuits. In addition, this
stereotypy has led to the notion that all neurons within the leech
CNS are unique (with the possible exception of the PE cells
(Baptista and Macagno, 1988), neurons that develop postembryonically in the ganglia of segments 5 and 6, which are the
reproductive segments of leeches).
The leech was developed as a neurophysiological preparation in the 1930’s by Gray et al. (1938), who studied the
neuronal bases of leech swimming and crawling. The first
intracellular recordings were accomplished in the early 1960’s,
when Hagiwara and Morita (1962) and Eckert (1963) recorded
intracellularly from the somata of the paired Retzius neurons in
segmental ganglia of Hirudo. They both showed convincingly
that these neurons are strongly electrically coupled. They also
demonstrated that at least two of the neurons in each ganglion
were identifiable by the location and size of their somata, as
well as their electrophysiological properties. The identification
and characterization of leech neurons advanced greatly when
John Nicholls chose to identify neurons that had been used in
the laboratory of Stephen Kuffler to characterize the electrophysiological properties of glial cells and their effects on the
electrical function of neurons (Kuffler and Potter, 1964). The
Nicholls lab identified mechanosensory neurons (Nicholls and
Baylor, 1968) and MNs (Stuart, 1970) in stereotyped locations
within each segmental ganglion. They showed that these
sensory neurons made both electrical and chemical synaptic
connections onto the MNs, and also established a number
of physiological techniques to suggest strongly that these
connections were direct, monosynaptic contacts, without any
intervening INs (Nicholls and Purves, 1970). Subsequently,
several other MNs have been identified (Ort et al., 1974;
Thompson and Stent, 1976a; Sawada et al., 1976; Norris
and Calabrese, 1987). Many of these MNs are inhibitory
(Stuart, 1970; Sawada et al., 1976; Ort et al., 1974), releasing
GABA onto muscle fibers to hyperpolarize them and cause their
relaxation (Cline, 1986). Surprisingly, at least some of the
inhibitory MNs also make strong central connections, with both
excitatory and inhibitory MNs (Ort et al., 1974; Granzow et al.,
1985; Granzow and Kristan, 1986; Friesen, 1989a), and with
INs (Friesen, 1989b).
1.2. The hydroskeleton and behaviors
Leeches perform a variety of distinguishable behaviors by
combinations of lengthening, shortening, and bending. Fig. 3
shows five of these behaviors: local bending, swimming,
whole-body shortening, crawling, and feeding. Each behavior
is produced by a characteristic temporal and spatial pattern of
muscle contractions. The nature of these motor patterns is
discussed below, in individual sections devoted to each of the
behaviors.
Leeches have no hard, fixed skeleton. Instead, they use a
muscular arrangement that has been termed a ‘‘muscularhydrostat’’ (Kier and Smith, 1985) or a ‘‘hydroskeleton’’ (Kristan
et al., 2000). The leech body is a tube whose shape is controlled
by muscles in each segment. To a first approximation, each
segment is a cylinder with an ovoid cross-section (Fig. 2B); a
segment maintains roughly the same volume during all
behaviors. The muscles used to produce the behaviors shown
in Fig. 3 are the longitudinal and circular layers in the body wall,
plus the dorsoventral muscles that span the body cavity.
Contraction of one muscle type changes the shape of the
cylinder by increasing internal pressure, stretching the other
muscle types. Hence, each of these three muscle types is
potentially an antagonist for the other two sets of muscles. The
body stiffness, which acts like a skeleton, is caused by cocontraction of antagonistic muscles or, for some behaviors, by
contraction of a fourth set of muscles, the oblique muscles. These
latter, thin muscles, which lie between the longitudinal and
circular muscles, are oriented obliquely, so that their contraction
stiffens the body at an intermediate body length – the posture seen
in a leech at rest. In effect, leeches have a degree of freedom not
available to skeletized animals: they can control the stiffness of
their skeleton dynamically during a behavior. A single segment
can perform four types of basic movements:
(1) bending, by contracting the longitudinal muscles on one
side; the longitudinal muscles on the opposite side may also
relax;
W.B. Kristan Jr. et al. / Progress in Neurobiology 76 (2005) 279–327
285
(2) shortening, by contracting all longitudinal muscles at the
same time;
(3) elongation, by contracting the circular muscles and
(4) flattening, by contracting the dorsoventral muscles. (Note
that flattening also produces elongation).
Fig. 3. Examples of leech behaviors. (A) Local bending: pressing on the skin at
any location around the leech’s surface (e.g. dorsal, ventral, lateral) causes
contraction of the longitudinal muscles at the site of the touch, and relaxation of
the longitudinal muscles on the opposite side. (B) Swimming: successive frames
of a film of a swimming leech, from the side, taken at 50 ms intervals. A quasisinusoidal wave of dorsoventral contractions moves from the front of the leech
(at left) to the back. The trough of the wave is produced by local contractions of
the dorsal longitudinal muscles, and the crest is produced by contractions of the
ventral longitudinal muscles. The whole body is strongly flattened throughout
the swim cycle. In a single segment, swimming consists of repeating alternations between dorsal and ventral longitudinal contractions. The rearward
progression of the traveling waves results from an intersegmental delay in
longitudinal muscle contractions. This delay varies in proportion to the cycle
period to maintain approximately one waveform in the body at all cycle periods
(the normal range being 0.4–2 s). The white dots at four locations along the
animal’s body are white beads sewn onto the lateral edge of the leech at specific
locations. The 12 frames shown constitute one swim cycle (note that the body
shape in the last frame is very similar to that seen in the first frame). (C) Wholebody shortening: when the front end of the leech is touched (top frame), it pulls
back rapidly (bottom frame) by contracting the longitudinal muscles in all body
segments simultaneously. (There is a short intersegmental delay, caused by
spike conduction between segments, but this delay is much shorter than the
delays seen in swimming.) This response is greatest when the animal is fully
elongated. (D) Crawling: schematic drawings of six characteristic stages of
vermiform crawling. In the top trace, the animal has shortened fully and
attached both suckers. The step begins with release of the front sucker (to
the right) and the beginning of a wave of elongation at the front end produced by
contraction of the circular muscles (second frame). The circular muscle-induced
elongation moves back along the animal until the body is fully extended; at this
point, the front sucker attaches (third frame). With both suckers attached, the
elongation at the front end is replaced by contraction (fourth frame), caused by
relaxation of the circular muscles and contraction of the longitudinal muscles.
This contraction moves back along the animal, pulling on the posterior end and
lengthening it. As the contraction wave moves through the back half of the
animal, the back sucker releases (fifth frame). The cycle is completed when the
contraction wave reaches the rear end and the back sucker is reattached (sixth
frame). (E) Feeding: shown is a typical posture during the consummatory phase
of feeding. The back sucker can be attached to the host or, as shown here, it can
be floating free. The front sucker is tightly attached to the skin surface and the
The first four behaviors illustrated in Fig. 3 are caused by
different temporal and spatial patterns of these segmental
movement units. Local bending (Fig. 3A) occurs in a small
number of adjacent segments (1). Whole-body shortening
(Fig. 3C) takes place in the whole animal almost simultaneously (2). In swimming (Fig. 3B), flattening of the whole
body (4) is maintained throughout swimming episodes, and
serves to stiffen the body and present a wide surface to the
water. During swimming, each segment alternately bends
dorsally and ventrally, with each segment producing the same
movement as its more anterior neighbor at a phase delay of
about 5%. This produces a repeated up-and-down undulation
with about one peak and one trough in the body at any given
time (Kristan et al., 1974a). Crawling (Fig. 3D) is also an
oscillatory locomotory pattern, but in this case shortening (2)
alternates with elongation (3) in each segment. Again, whatever
occurs in a given segment is repeated in the next segment with a
delay of about 5% of the cycle period. Compared to swimming,
crawling cycles are slow: swim cycles are 0.4–2.0 s in duration
(Kristan et al., 1974a,b; Kristan and Calabrese, 1976), whereas
crawling cycles are 2–20 s in duration (Stern-Tomlinson et al.,
1986; Cacciatore et al., 2000). The action of the suckers is
important in shortening and crawling, but very little is known
about their muscular or neuronal control. The typical feeding
posture (Fig. 3E) is with one or both suckers attached. The jaws
evert through the front sucker, rasp a hole in the skin of the prey,
and blood is sucked through the oral opening in this sucker.
Specialized internal muscles in the pharynx produce the suction
that brings the blood into the body, but longitudinal muscles
produce a peristalsis that moves blood into the various chamber
of the gut (Wilson and Kleinhaus, 2000).
2. Circulation and heartbeat
Heartbeat is an autonomic function that is rhythmic and
continuous in Hirudo medicinalis. The circulatory system is a
closed network comprising four longitudinal vessels – one
dorsal, one ventral, and two lateral – that run the length of the
animal, communicating in every segment by a series of
branched vessels (Fig. 2A). Rhythmic constrictions of the
muscular lateral vessels (the heart tubes) drive the flow of blood
through the closed circulatory system (Thompson and Stent,
1976a). The heart muscle can generate a myogenic rhythm of
contractions, which are normally entrained by patterned
rhythmic motor outflow from segmental ganglia. The hearts
are coordinated so that one beats in a rear-to-front progression
anterior end is held in a characteristic, rigid posture with pulsations at 2–4 Hz
reflecting the sucking movements made by the pharyngeal muscles in the
anterior end. The rest of the body produces slower movements, either undulations or peristaltic movements (see Fig. 25) that move the ingested blood into
the gut pouches (see Fig. 2A). Scale bar, 2.5 cm.
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(peristaltically), whereas the other one beats synchronously
along most of its length. The peristaltic side produces high
systolic pressure and forward flow of blood in the heart tubes,
whereas the synchronous side produces low systolic pressure
and blood flow into the peripheral circulation (Krahl and
Zerbst-Boroffka, 1983; Hildebrandt, 1988; Wenning et al.,
2004a,b).
2.1. The heartbeat neural control system of the leech
The hearts are innervated in each segment by heart
excitatory (HE) MNs, a bilateral pair of neurons found in
each of the third through 18th segmental ganglia (i.e. M3–M18)
(Thompson and Stent, 1976a,b). Each HE MN contacts heart
muscle directly and forms conventional cholinergic neuromuscular junctions (Maranto and Calabrese, 1984a,b; Calabrese and Maranto, 1986). The heart MNs are rhythmically active
(Fig. 4C); they entrain the myogenic rhythm of the heart
through rhythmic excitation. Thus the spatio-temporal activity
pattern of the segmental heart MNs (the motor pattern)
determines the constriction pattern of the hearts. This motor
pattern is organized by a central pattern generator (CPG): the
isolated ventral nerve cord continuously produces a heartbeat
pattern that is similar in period and spatial pattern to the heart
constrictions seen in the animal (Fig. 4C) (Calabrese and
Peterson, 1983). The rhythmic activity pattern of the heart MNs
derives from the cyclic inhibition that they receive from this
CPG (Fig. 4). When these inhibitory inputs to the heart MNs are
blocked by bicuculline, the MNs fire at a steady rate (Schmidt
and Calabrese, 1992).
There is an asymmetry in the heart constriction pattern that
arises from an asymmetry in the motor pattern (Thompson
and Stent, 1976b). That is, the same coordination modes –
peristaltic and synchronous – observed in the two hearts are
also seen in the HE MNs. The HE MNs on one side are active in
a rear-to-front progression, while the HE MNs on the other are
active nearly synchronously along most of the nerve cord
(Calabrese and Peterson, 1983; Wenning et al., 2004b), and the
coordination of the HE MNs along the two sides switches
approximately every 20–40 heartbeat cycles (Thompson and
Stent, 1976b; Wenning et al., 2004a,b). Because switching
between coordination states is produced by the isolated nerve
cord, a CPG must produce this switching, too (Gramoll et al.,
1994; Wenning et al., 2004b).
The pattern generator comprises seven bilateral pairs of
identified heart INs (HNs) that occur in the first seven
segmental ganglia (Thompson and Stent, 1976c; Calabrese and
Peterson, 1983). The connections made by heart INs are largely
inhibitory; they inhibit each other, and those in M3, M4, M6,
and M7 inhibit heart MNs (Fig. 4A and B).
2.2. The elemental oscillators
Because passing current into any one of the first four pairs of
heart INs can reset and entrain the rhythm of the entire network
of INs (Peterson and Calabrese, 1982), the heart INs in M1–M4
constitute the timing network of the heartbeat pattern generator
Fig. 4. Activity and synaptic connectivity of heart excitatory (HE) MNs and
heart interneurons (HN INs). (A) Circuit diagram showing the inhibitory
synaptic connections from identified HN INs to HE MNs. (B) Circuit diagram
showing the inhibitory synaptic connections among all the identified HN INs.
Neurons with the same input and output connections are lumped together. In all
circuit diagrams, large unfilled circles represent neurons (each identified by the
number of its ganglion) and the lines represent major neurites or axons. Small
filled circles represent inhibitory chemical synapses. (C) Simultaneous intracellular recordings showing the normal rhythmic activity of two reciprocally
inhibitory oscillator heart INs of midbody ganglion 4, HN(R,4) and HN(L,4),
and a heart MN HE(R,5) postsynaptic to HN(R,4) in an isolated nerve cord
preparation. Dashed lines indicate a membrane potential of 50 mV. In all
figures, the body side and ganglion number are indicated as in the following
example: cell HN(L,1).
(Fig. 4B). The other three pairs of heart INs are followers of
these anterior pairs. Two foci of oscillation in this beat timing
network have been identified in M3 and M4 (Peterson, 1983a).
Reciprocally inhibitory synapses between the bilateral pairs of
heart INs in these ganglia (Figs. 4B and 5A), combined with the
intrinsic membrane properties of these neurons pace the
oscillation (Peterson, 1983a,b; Cymbalyuk et al., 2002). Thus
each of these two reciprocally inhibitory heart interneuronal
pairs is an elemental half-center oscillator (Figs. 4B and 5A)
and the M3 and M4 heart INs are called oscillator INs.
2.3. Mechanisms of oscillation in an elemental half-center
oscillator
Voltage-clamp studies have identified several intrinsic
currents that contribute to the oscillatory activity of oscillator
INs. These include:
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287
oscillator IN. Release from inhibition results from a waning of
the depolarization in the active oscillator IN due to the slow
inactivation of its ICaS, which slows its spike rate and thereby
reduces its spike-mediated inhibition of the contralateral
oscillator IN. A model of the half-center interactions was
tested by voltage clamping oscillator INs using waveforms that
mimic the slow wave of oscillation (Olsen and Calabrese,
1996). Quantitative estimates of Ih, IP, and the low-threshold
Ca2+ currents, as well as their trajectories during oscillation,
demonstrated that Ih has a negative feedback relationship with
period. Perturbations that produce a longer cycle period
increase Ih, thereby shortening the cycle period, whereas
perturbations that produce a shorter period decrease Ih, which
increases the cycle period. Thus Ih homeostatically regulates
the heartbeat cycle period.
Fig. 5. Circuit diagram and electrical activity of the heartbeat-timing network.
(A) The timing network consists of paired heart INs (HN) in the first four
midbody ganglia (M1–M4). The first and second ganglia are represented as a
single ganglion for simplicity. Large unfilled circles represent neurons (each
identified by the number of its ganglion of origin) and the lines represent major
neurites or axons, squares are distal sites of spike initiation, and small filled
circles are inhibitory synapses. (B) Activity of a segmental oscillator. Simultaneous extracellular recordings of the paired oscillator INs in M3 made with a
suction electrode placed snugly over the soma of each neuron. The two INs
alternate in their activity. (C) Coordinated activity of the timing network.
Simultaneous extracellular recordings of the coordinated activity of ipsilateral
heart INs in M2–M4 made with suction electrodes. The oscillator INs in M3 and
M4 are active nearly in-phase but with a perceptible phase lag of about 10% of
the cycle period, while the coordinating IN is active in anti-phase (after Masino
and Calabrese, 2002a). Please note that the literature on heartbeat uses an older
designation for the midbody segmental ganglia: G1–G21, rather than M1–M21.
a fast Na+ current that mediates spikes;
two low-threshold Ca2+ currents [one rapidly inactivating
(ICaF) and one slowly inactivating (ICaS); Angstadt and
Calabrese, 1991];
three outward currents [a fast transient K+ current (IA) and
two delayed rectifier-like K+ currents, one inactivating (IK1),
and one persistent (IK2); Simon et al., 1992];
a hyperpolarization-activated inward current (Ih), a mixed
Na+/K+ current with a reversal potential of
20 mV
(Angstadt and Calabrese, 1989);
a low-threshold persistent Na+ current (IP) (Opdyke and
Calabrese, 1994).
The inhibition between oscillator INs consists of a graded
component that is associated with the low-threshold Ca2+
currents (Angstadt and Calabrese, 1991) and a spike-mediated
component associated with high-threshold Ca2+ current (Simon
et al., 1994; Lu et al., 1997; Ivanov and Calabrese, 2000).
Spike-mediated transmission is sustained during normal
bursting (Nicholls and Wallace, 1978; Ivanov and Calabrese,
2003), while graded transmission wanes during a burst because
the low-threshold Ca2+ currents inactivate (Angstadt and
Calabrese, 1991). Modeling studies based on these biophysical
measurements indicate that oscillation in an elemental half
center oscillator is a subtle mix of escape and release (Fig. 6;
Nadim et al., 1995; Olsen et al., 1995; Hill et al., 2001). Escape
from inhibition is due to the slow activation of Ih in the inhibited
2.4. Coordination in the beat timing network
The heart INs in M1 and M2 act as coordinating INs, serving
to couple the two elemental oscillators (Fig. 5A). Together with
the HN cells in M3 and M4, these INs form a beat timing
network that paces the pattern generator and establishes the
underpinnings of intersegmental coordination (Fig. 5A; Peterson, 1983a,b; Masino and Calabrese, 2002a). The INs in M1
and M2 do not initiate spikes in their own ganglion; instead they
have two spike initiating sites, one in M3 and the other in M4.
Normally, the great majority (>85%) of spikes in the
coordinating neurons are initiated in M4, but under certain
conditions spikes can also be initiated in M3 (Peterson,
1983a,b; Masino and Calabrese, 2002a). For example, when
M3 and M4 are separated by cutting the connective, the
processes of the coordinating neurons in M3 will initiate spikes.
Thus, isolated M3 and M4 each contain a segmental oscillator
that consists of a pair of reciprocally inhibitory oscillator INs
(elemental half-center) and the active stumps of processes from
the coordinating neurons, which provide additional inhibition
(Hill et al., 2001). (Invertebrate axons may have neuritic
branches (input and output) at several sites within the nervous
system and thus have multiple, electrotonically distant sites of
synaptic input, spike initiation, and synaptic output (Perrins and
Weiss, 1998; Coleman et al., 1995).) The coupling between the
M3 and M4 segmental oscillators causes the M3 and the M4
oscillator INs on the same side to be active roughly in phase
(Fig. 5C).
In an isolated beat timing network (M1–M4), the phase
relationship between the oscillators is constant (i.e. their
activity is phase-locked) but varies among preparations and
whether the preparation is exposed to modulator substances. In
the majority of unmodulated preparations, the M4 oscillator
leads the M3 oscillator (Fig. 5C; Peterson, 1983a,b; Masino and
Calabrese, 2002a). Because both the M3 and M4 oscillator INs
provide inhibitory input to heart MNs, the phase relationships
between the M3 and M4 segmental oscillators is important in
determining the HE MN activity pattern. When the M3 and M4
segmental oscillators were reversibly uncoupled by blocking
axonal conduction in the connectives between M3 and M4 with
a sucrose solution, the ‘‘intact’’ phase difference proved to be
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Fig. 6. Synaptic conductances and some major intrinsic currents that are active during a single cycle of a two cell (half-center) heart IN oscillator model (Hill et al.,
2001). (A) Biological neurons. (B) Model neurons. The graded synaptic conductance (gSynG) is shown at the same scale as the total synaptic conductance (gSynTotal),
which is the sum of the graded and spike-mediated conductances. The slow calcium current (ICaS), the hyperpolarization-activated current (Ih), and the persistent
sodium current (IP) are shown to the same scale. Note that IP is active throughout the entire cycle period.
determined by the difference between the ‘‘isolated’’ periods in
the segmental oscillators: the faster oscillator leads in phase,
and the phase difference is nearly linearly related to the period
difference (Masino and Calabrese, 2002c). Both the period
difference between the segmental oscillators as well as their
phase differences can be changed using neuromodulators and
pharmacological agents (Masino and Calabrese, 2002b). Again,
in these experiments, the period of the intact beat timing
network was the same as the period of the faster segmental
oscillator (Masino and Calabrese, 2002a,b; Hill et al., 2002).
This rate dominance by the faster oscillator is likely to result
from the faster oscillator beginning to fire earlier in each cycle,
thereby silencing the ipsilateral coordinating INs and removing
their inhibition onto the slower oscillator, thus speeding up the
slower oscillator’s rhythm. The feasibility of this conceptua-
lization has been shown in modeling studies (Hill et al., 2002,
2003; Jezzini et al., 2004).
2.5. Heartbeat motor pattern switching by switch
interneurons
Switching between the peristaltic and synchronous modes
(Fig. 7A) is accomplished by a pair of switch INs whose somata
are in M5. The M3 and M4 oscillator INs on one side inhibit the
switch heart IN on the same side (Figs. 4B and 7B) (Thompson
and Stent, 1976c; Calabrese, 1977). These switch INs inhibit
both heart INs in M6 and M7. Only one of the switch INs
produces impulse bursts during any given heartbeat cycle; the
other switch IN is silent, although it receives rhythmic
inhibition from the beat timing oscillator (Fig. 7B; Calabrese
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289
Fig. 7. Switches in coordination state in the central motor program for heartbeat. (A) Continuous extracellular records from the vascular nerves (VN) of three
segmental ganglia of an isolated nerve cord. The records are indexed for segment and body side as indicated in Fig. 4. The bursts of impulses recorded on the vascular
nerves result from activity in the axons of the heart HE MNs. Small arrowheads at the beginning and end of the record indicate the starts of bursts. The record begins
with the right side coordinated peristaltically (note rear-to-front progression in the start of the heart MN bursts) and the left side coordinated synchronously. At the
large arrowheads a switch in coordination state occurs so that at the end of the record the right side is coordinated synchronously and the left is coordinated
peristaltically (after Calabrese and Peterson, 1983). (B) Simultaneous intracellular recordings from two switch heart INs of ganglion M5 show that only one is
rhythmically active at a time; the other is inactive. The large arrowheads indicate a spontaneous reciprocal switch of their activity states (after Lu et al., 1999). (C)
Circuit diagrams of the heartbeat pattern generator and the phase relations among the heart INs (HN cells) before and after the switch illustrated in B. Circuit diagrams
are like that of Fig. 4B. Neurons that are stippled fire approximately in phase with one another and in antiphase with those that are not stippled. The system has two
different coordination states, depending on which of the two switch heart INs is active and which is inactive (dashed line) (Calabrese, 1977; Gramoll et al., 1994; Lu
et al., 1999). The coordination of MNs anterior to ganglion M7 is also controlled by the switch IN, which unilaterally drives an unidentified premotor IN in a nearly
one-to-one fashion (Calabrese, 1977). The small phase differences (5–10%) between the HN(3) and HN(4) neurons and the between the HN(6) and HN(7) INs are not
illustrated.
and Peterson, 1983; Gramoll et al., 1994; Lu et al., 1999). With
a period approximately 20–30 times longer than the period (8 s)
of the heartbeat cycle, the silent switch IN is activated and the
previously active one is silenced. The activity of the switch INs
determines which side is in the peristaltic versus the
synchronous activity mode (Fig. 7B). The switch INs link
the timing oscillator in M1–M4 to the M6 and M7 heart INs.
Because only one switch IN is active at any given time, there is
an inherent asymmetry in the coordination of the heart INs on
the two sides: the ipsilateral M3, M4, M6, and M7 heart INs are
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active roughly in phase on the side of the active switch IN,
whereas the ipsilateral M3 and M4 INs are active out of phase
with the M6 and M7 INs on the side of the silent switch IN
(Fig. 7C). Hence, the heart MNs are coordinated synchronously
on the side of the rhythmically active switch IN, whereas the
MNs are coordinated peristaltically on the side of the silent
switch IN. The observed switches in the coordination state of
the heart MNs, therefore, reflect switches in the activity state of
the switch INs (Fig. 7B; Thompson and Stent, 1976c;
Calabrese, 1977).
Is the switch in the activity states of the switch INs
controlled by an independent switch timing network or is the
switching mechanism inherent to these neurons themselves?
There appears to be no synaptic connections between the switch
INs, because injecting current to change the activity in one
switch IN does not influence the activity state of the other, even
though spontaneous switches in the activity state are always
reciprocal (Lu et al., 1999). Voltage-clamp studies showed that
in the silent state, switch neurons have a persistent outward
current that is not voltage-sensitive and reverses around
60 mV (Gramoll et al., 1994). This current turns off in a
switch to the active state. Thus, in its silent state, the switch IN
is inhibited by a persistent leak current. The dynamic-clamp
(Sharp et al., 1993) was used to simulate the turn-on and turnoff of such a leak, which was found to be sufficient to change
the activity state of the manipulated switch IN (Gramoll et al.,
1994; Lu et al., 1999). These results argue that switching is
controlled by an independent timing network, extrinsic to the
switch neurons, that alternately imposes a tonic inhibitory leak
alternately on one of the two switch INs. This network remains
unidentified.
2.6. Gaps in our current knowledge
The heartbeat CPG can be conceptualized as two timing
networks: a beat timing network comprising the first four pairs
of heart INs (two oscillator pairs and two coordinating pairs)
and a switch timing network that governs the activity of the
switch INs. The two timing networks converge on the switch
INs, and together with the heart INs in M6 and M7, make up the
heartbeat CPG (CPG). The output of the CPG is configured into
two coordination states of heart MNs by the alternating activity
states of the two switch INs. Although much remains to be done
to understand the dynamics of the activity pattern of the INs in
the heartbeat CPG and how these dynamics translate into the
HE MN output pattern, the problems presented by such a
synthesis seem tractable.
3. Overt behaviors
3.1. Introduction
Four of the leech behaviors – local bending, whole-body
shortening, crawling, and swimming – can be elicited by tactile
stimulation of the leech. The fifth – feeding – is elicited by
chemical, tactile, and thermal stimuli. Which behavior is
elicited by tactile stimulation depends upon the location and
intensity of the touch. Such studies have used a calibrated
touching device (Lewis and Kristan, 1998c), electrical
stimulation of the skin (Kristan et al., 1982), and intracellular
depolarization (Nicholls and Baylor, 1968) to activate
mechanoreceptive T (touch), P (pressure), and N (nociceptive)
cells. One generality from these studies is that activating
T cells elicits the same behaviors as activating P cells, but
P cells are more effective than T cells. That is, fewer P cells
need to be activated at lower frequencies to produce the same
behavioral responses as T cells. N cell activation also evokes
swimming behavior, but can also cause qualitatively different
responses, such as writhing and flailing, which have yet to be
studied.
At a threshold level of mechanosensory stimulation, the
predominant response elicited depends upon the location of the
stimulus: stimulating the anterior end produces shortening,
stimulating the posterior end produces crawling or swimming,
and stimulating midbody sites produces local bending (Kristan
et al., 1982). Over some range, increasing the stimulus intensity
merely increases the intensity of the response. At high
stimulation frequencies (in the range of 20–40 Hz), however,
the responses change qualitatively. Strong stimuli to the front
and back often produce sequential responses, with a violent
version of the primary response followed by a vigorous
secondary response. For instance, stimulating strongly the front
end often produces a vigorous shortening followed by a rapid
elongation that might lead to crawling or swimming (Kristan
et al., 1982). Increasing the intensity of stimulation to a middle
segment produces progressively stronger and more extensive
local bending, in which more remote segments anterior and
posterior to the one stimulated stiffen, so that the bend of the
segment being stimulated becomes more exaggerated and
widespread (Wittenberg and Kristan, 1992a). At even higher
stimulation intensities, the behavior switches to a whole-body
response, such as curling or twisting (Kristan et al., 1982).
The following sections summarize what is known about each
of the neuronal circuits underlying the five individual
behaviors. Local bending and shortening are purely reflexive,
episodic responses to a well-defined stimulus. Swimming,
crawling, and feeding are rhythmic behaviors that, like the
heartbeat system described above, are produced by CPGs that
can also produce the underlying motor pattern in the isolated
nervous system (Fig. 1B). This part includes a section on
interactions among the behaviors, emphasizing how individual
neurons are used in more than one behavior.
3.2. Local bending
3.2.1. Motor neurons
In response to a light touch to the skin of a segment in the
middle of a leech (Fig. 8A), that segment produces shortening
on the side touched and lengthening of the side away from the
touch (Fig. 3A). A segment produces this behavior by
contracting longitudinal muscles on the side of the touch
and elongating those on the opposite side, after initially
contracting both sides (Fig. 8B; Kristan, 1982). (The
contribution of circular muscles is discussed below.) Mono-
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Fig. 8. Behavior and neuronal bases of local bending. (A) The semi-intact
preparation used to characterize local bending. One lateral half of a leech’s body
wall, from dorsal midline to ventral midline, is removed and pinned out on one
end. Two tension transducers are attached to the other end to measure tension
generated by the dorsal and ventral longitudinal muscles. The dark dots along
three of the annuli indicate the sites of the sensilla, located along the middle
annulus in each segment. One ganglion is left attached to the piece of body wall
by its nerves; it is pinned out to allow recording from individual MNs. A
stimulating electrode is placed on the skin to excite the terminals of the
mechanosensory neurons innervating the mid-dorsal region of the body wall
in the middle segment. (B) Tension produced by dorsal and ventral longitudinal
muscles in response to stimulating the dorsal skin. The stimulus activated
sensory neurons for a short time, indicated by the bar (stim.) under the tension
traces. Initially, both dorsal and ventral longitudinal muscles contracted, but the
ventral muscle started to relax in less than 1 s, while the dorsal longitudinal
muscle continued to contract for several more seconds. If the ventral skin had
been stimulated the pattern would be reversed. (C) Recordings from excitatory
and inhibitory MNs to longitudinal muscles during a local bend response. The
same kind of electrical stimulus was given to the dorsal skin while recording
from four different MNs. (The recordings were made successively; they are
lined up relative to the stimulus to indicate their activity during a single
response.) DE is an excitor MN to the dorsal longitudinal muscles; VE is an
excitor of the ventral longitudinal muscles; DI is an inhibitor of the dorsal
longitudinal muscles, and VI is an inhibitor of the ventral longitudinal muscles.
Note that stimulating the dorsal skin activates DE and inhibits VE, as expected
from the tension recordings. The inhibitors, DI and VI, show the opposite
response. The motor neuronal responses are entirely consistent with the tension
recordings except that VE shows only inhibition; it does not show the initial
excitation apparent in the Ventral tension trace in part B. This initial excitation
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Fig. 9. Local bend circuitry. (A) Simplified schematic summary of connections.
Pressure-sensitive mechanoreceptors innervating dorsal and ventral body wall
(PD and PV cells, respectively), with overlapping receptive fields, sense pressure
to the skin. They transmit this activity, via excitatory synapses, to a layer of INs
that excites longitudinal MNs. The inhibitory MNs, in addition to inhibiting
muscles, also inhibit the excitors of the same muscles. This central inhibition
provides the only known inhibitory input to the MNs during local bending. (B)
Schematic summary of the central connections. All four P cells connect to all 17
known INs, although the strengths of these connections produce cosine-shaped
receptive fields for the INs. The connections from the cell immediately above
(in the same vertical column) are strong, and the strength diminishes with lateral
distance. For instance, cell 115 receives strong synaptic input from the
ipsilateral Pd cells but very weak connections from the contralateral Pv cell.
Note that inhibitory MNs receive inputs that are the same as for the excitatory
MNs of the opposite sign. For instance, DI (in this figure, called ‘‘iMNd’’)
receives the same input as the DE MNs (‘‘eMNd’’). This connectivity produces
responses seen in Fig. 8C. In both diagrams, inhibitory connections are
indicated by filled circles and excitatory connections by lines. All excitatory
connections are feed-forward, from P cells to INs and from INs to MNs.
synaptic connections from the T and P mechanosensory cells
onto the L MNs (each of which activates all the longitudinal
muscles in one side of the body wall) cause the initial cocontraction. Polysynaptic connections onto MNs with more
restricted motor fields (e.g. dorsal excitor neuron cell 3 (DE-3)
excites a band of dorsal longitudinal muscles and ventral
of the dorsal and ventral longitudinal muscles, in fact, is produced largely by the
activation of the L cells, MNs that cause strong contraction in all the longitudinal muscles on one side of a segment. The L cells are activated only during
the stimulus, so that the initial response to the stimulus is, as previously
surmised from neuronal connections (Nicholls and Baylor, 1968), a shortening
response. Activity in the more localized MNs (i.e. DE and VE) persists far after
the stimulus; this persistent activity is the cause of the local bend.
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Fig. 10. Summary of receptive fields of mechanosensitive P cells, INs, and MNs. The left column (panels A, C, and E) shows data used to determine the receptive
fields obtained from recordings of the different neurons. The maximal response location was calculated for all the neurons by fitting a cosine function to their stimulus
response magnitudes at several locations within their receptive fields. The data were plotted relative to the peak location (08). Receptive fields for the P cells were
determined from preparations illustrated in Fig. 8A. Stimulating each of the four P cells and plotting the amplitude of the response, as a function of the locations of the
middle of the P cell receptive fields, determined receptive fields for INs and MNs. In all cases, the data were normalized to the maximal response for each neuron. The
panels in the right column (B, D, and F) are idealized summaries of all the receptive fields for all the neurons of each kind, which were used to simulate the function of
the system. (A) Touch (T) and pressure (P) cell receptive fields. The curve is the best-fit cosine function to all data. (B) The best-fit cosine functions for each of the four
P cells, each centered on the location of the maximal response. (C) Local bend IN receptive fields, with the best-fit cosine curve. Normalized data are shown for four
W.B. Kristan Jr. et al. / Progress in Neurobiology 76 (2005) 279–327
excitor neuron cell 4 (VE-4) excites a ventral longitudinal band)
produce the more prolonged excitatory response on the side of
stimulation (Fig. 8C). The same stimulation also activates the
inhibitory MNs on the side away from the touch, thereby
inhibiting both longitudinal muscles and the excitatory MNs to
the longitudinal muscles during the contraction on the side of
the touch (Lockery and Kristan, 1990a).
This inhibition could be produced either by direct inhibitory
connections from the local bending INs (LBIs), or by excitatory
connections onto appropriate inhibitory MNs. Tests showed
that when the inhibitory MNs synapsing onto DE-3 were
hyperpolarized during ventral stimulation, the input onto DE-3
was excitatory (Lockery and Kristan, 1990b). This experiment
suggests that LBIs make only excitatory connections onto MNs,
and that the only inhibition in the pathway is the one made by
the inhibitory MNs onto the excitatory MNs (and muscles;
Fig. 9A). Because there are neither feedback connections (e.g.
from INs to sensory cells, or MNs back onto INs) nor any
functionally important lateral connections at the sensory or
interneuronal level, the first two layers of the local bending
system form an excitatory, feed-forward circuit (Fig. 9B). The
third layer provides the only lateral connections, namely the
inhibitory connections made by the inhibitory MNs onto the
antagonistic excitors. These inhibitory connections provide the
reciprocity of the response; inhibitory MNs to a given
longitudinal muscle (e.g. the left dorsal) receive the sensory
input that is identical to that for the excitatory MNs on the
opposite side (i.e. the right ventral) and relax the muscles on the
side contralateral to the touch.
3.2.2. Mechanosensory neurons that produce local bending
Two lines of evidence indicate that the P cells are the most
important mechanosensory neurons for producing the local
bend response. First, monitoring responses of T and P cells to
tactile stimuli of different intensities showed that the amplitude
of the local bend tracked the responses of P cells but not T or N
cells (Lewis and Kristan, 1998c). Second, stimulating a single P
cell produces a response that is essentially the same as
stimulating the skin in the middle of that P cell’s receptive field
(Kristan, 1982; Lewis and Kristan, 1998a). Hence, to determine
the neuronal bases of local bending, the activity of the P cells is
the most important. The receptive field of each P cell is the top
of a cosine function, cut off at 908 (Fig. 10A), taking the body
circumference as 3608. The scatter of the points around this
curve shows that there is considerable variability in the
responses at different locations even when the site and
magnitude of the stimulus is kept the same (Lewis and Kristan,
1998c; Lewis, 1999). Adjacent P cells form overlapping
receptive fields that span about half the body circumference,
with peak responsiveness in the middle of the dorsal and ventral
quadrants on each side (Fig. 10B).
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3.2.3. Local bend interneurons
INs involved in local bending, the LBIs, were located by
recording intracellularly from many of the 400 neurons in
individual segmental ganglia, searching for neurons that (1)
received strong input from a P cell with a dorsal receptive field
(PD), and (2) activated the dorsal MN, DE-3 (Lockery and
Kristan, 1990b). Seventeen such INs were found, six of which
were bilaterally paired and one of which was unpaired. By
stimulating each of the P cells individually and in pairs
(Lockery and Kristan, 1990b), the receptive fields of each IN
were inferred (Lewis, 1999). The receptive fields of each LBI
could be nicely fit by a cosine function, and, because each LBI
receives input from all the P cells, their receptive fields span the
whole circumference of the body (Fig. 10C), that is, twice the
width of the P-cell receptive fields. The center of the receptive
field for each identified LBI is characteristic for that IN, and the
locations of the centers are distributed fairly evenly around the
dorsal and lateral body wall (Fig. 10D). In the search for LBIs,
none were found to have ventral receptive fields (Lockery and
Kristan, 1990b). This is likely the result of the search strategy,
which was directed specifically to find INs with dorsal inputs
and outputs. Because LBIs with ventral fields would be only
weakly activated by PD inputs and would not activate DE MNs,
they could easily have been missed in the original search. If the
search was thorough, another three or four pairs of ventral LBIs
might yet be identified.
The connections from LBIs to MNs were inferred from the
responses of MNs to stimulating P cells, individually and in
adjacent pairs. Because each MN receives input from every P
cell, their receptive fields – like those of the LBIs – span the
entire circumference of the body wall (Fig. 10E). Also like the
LBIs, the receptive fields of the MNs are well characterized by a
cosine function. Unlike the LBIs, however, the receptive fields
have an inhibitory component from the P cells whose receptive
fields are on the side opposite to fields activated by each MN.
This inhibition ensures that the side opposite to the location of a
touch relaxes, allowing the bend to be more effective in moving
the body away from an object touching it. As discussed above,
the direct LBI input onto the MNs appears to be exclusively
excitatory, but this excitation is overridden by indirect
inhibition via the inhibitory MNs (Fig. 9B). The receptive
fields of the MNs, like those of the P cells and INs, are
distributed uniformly over the body wall. In fact, the tactile
receptive fields of the MNs are very similar to their motor units.
For instance, excitatory dorsal longitudinal MNs have receptive
fields centered on the dorsal surface and excitatory ventral MNs
have receptive fields centered on the ventral surface. It is this
coherence of sensory and motor fields, in fact, that provides the
localization of the local bend. The relaxation of the side
opposite to the touch results from the disjunction in the sensory
and motor fields of the inhibitory MNs. For example, the
LBIs (cells 115, 125, 212, and 218) in response to stimulating each of the four P cells. (D) The idealized receptive fields for all the known and hypothesized INs. (E)
Motor neuron receptive fields. Normalized synaptic responses from the four P cells for both excitors (cell 3 is a DE, cell 4 is a VE) and inhibitors (cell 1 is a DI, cell 2 is
a VI) are shown, with the cosine function that best fits the data for all the motor neurons. (F) Idealized receptive fields for the longitudinal muscle MNs, with the peaks
lined up by the locations of their maximal responses. (G) The approximated motor fields of the longitudinal MNs used in modeling the function of the local bending
circuit. All connections are excitatory to the three or four sections of longitudinal muscles (from Lewis and Kristan, 1998b). These are approximations of the
innervation fields of the MNs (Stuart, 1970).
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Fig. 11. Vector model matches local bending behavior. (A1) The direction of 16 local bending responses to identical stimuli delivered to the mid-dorsal skin, 458 from
the dorsal midline (arrow). The dashed line indicates the average response to the 16 stimuli. The angular difference between the arrow and the dashed line is the error
of the response. (A2) The locations of 12 responses to repeated bursts of intracellular stimulation delivered to the right ventral P cell that were similar to the train of
impulses seen when the middle of the receptive field of this neuron was touched. These stimulus trains produced an average response (dashed line) very close to the
middle of the receptive field of this neuron, which is the expected response to activation of a single P cell. (From Fig. 10B, the only location at which a touch would
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sensory receptive fields of the dorsal inhibitors are centered on
the ventral surface of the opposite side, and the ventral
inhibitors are most strongly activated by touching the dorsal
skin on the opposite side.
3.2.4. The local bend response as a directed behavior
The characteristics of the local bend circuit (broadly tuned
neurons with overlapping receptive fields) provide an ideal way
for a neuronal population to perform a vector estimation of the
location of a stimulus (Salinas and Abbott, 1995) or for
calculating the direction of a movement controlled by those INs
(Georgopoulos et al., 1988). The schematic summary diagrams
in Fig. 10B, D, F, and G emphasize the fact that touching a
particular location produces shortening centered at that
location. Behavioral tests showed that the response was
centered to within 8% of the circumference of the stimulated
segment (Lewis and Kristan, 1998b), despite the fact that the
responses of only four sensory neurons are used to make this
localization. This precision is possible by using the relative
firing rates of overlapping sensors that are evenly distributed
over the circumference. This population code is maintained
through the LBI layer to the MNs. A simple model of this
network, using the amount of variability seen in the responses at
the sensory level, showed that this network was sufficient to
explain the precision shown by the animal’s localized bending
response to local stimulation (Lewis and Kristan, 1998a). In
addition, the model matched the accuracy of the response to
stimulation at two locations (Fig. 11). This experiment
confirmed that individual P cells contribute strongly to the
direction of the response as well as showing that a vector
population code is important in determining response direction.
Further analyses made three additional points:
(1) Very few P cell spikes – less than five typically – were
required to determine the location of the response (Lewis
and Kristan, 1998c).
(2) The response direction was determined tens of milliseconds
before the response began. This shows that sensory
feedback was not required for determining response
location (Lewis and Kristan, 1998c).
(3) The number of INs required to determine the response
direction depends critically upon the noise in the system.
The number of INs used – about 24 – nicely compensates
for the level of noise found in the system (Lewis and
Kristan, 1998a).
More recent studies have used an optic flow algorithm
developed for video surveillance to plot local bending
movements very precisely (Zoccolan et al., 2001). A vector
field analysis of optic flow data, showed that circular muscles
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contribulte significantly to the local bend (Zoccolan and Torre,
2002). Interestingly, a linear combination of the contributions
of circular and longitudinal MNs matched the observed
responses to mechanosensory stimulation very well. In
addition, applying this much finer-grained analysis to
stimulating individual T, P, and N cells confirmed that the
major contribution to local bending is from the P cells. On a
more global scale, this analysis showed that, despite a large
variability in the individual MN responses, the contraction
phase of local bending was quite reproducible, because the
variability between MNs was independent (Zoccolan et al.,
2002). The relaxation phase of the response, however, was
much more variable, probably due to variabilty in the duration
of the responses of the INs and in the biomechanics of the
muscles (Garcia-Perez et al., 2004).
By using principle component analysis on optic flow data,
the local bend response was shown to discriminate the location
of two tactile stimuli as well as humans can do with their finger
tips (Baca et al., 2005). As suggested previously (Lewis and
Kristan, 1998c), this analysis also showed that the location of
the stimulus is coded within the first 200 ms of the stimulus
(before the movement actually starts), so that more prolonged
stimuli did not produce better localization (Baca et al., 2005).
Longer stimuli did, however, produce larger responses,
suggesting that response location and response magnitude
are coded somewhat differently. Because its neuronal circuitry
is so well defined, local bending is an ideal behavior for testing
ideas about sensory coding.
Neural net models of local bending behavior have been used
to investigate both the feasibility of using a distributed network
to perform local bending (Lockery et al., 1989; Kristan, 2000)
and the influence of synaptic plasticity on local bending
(Lockery and Sejnowski, 1992). A very interesting, although
somewhat troubling, conclusion from these neural net models is
that synaptic changes large enough to produce significant
behavioral changes could be undetectable electrophysiologically, if they were distributed over many neurons (Lockery and
Sejnowski, 1993). A more optimistic outcome of these models
is that population coding schemes make it possible for a small
number of neurons to produce several different behaviors,
provided that they are activated by different sensory neurons or
INs (Lockery and Sejnowski, 1992; Kristan, 2000). A more
physiologically realistic model (Baca and Kristan, 2001)
showed that the inhibition provided by the inhibitory MNs
could both produce lateral inhibition and smooth out the
responses in the coarse representation of location by the P cells.
3.2.5. Gaps in our current knowledge
The local bend response arises from a relatively simple,
mostly feed-forward system that pulls the body away from the
activate only one P cell is in the middle of its receptive field, i.e. at 458.) The responses had less variability than did stimulation of the body wall (e.g. the left panel)
because the variability of the P-cell response was eliminated by the electrical stimulation. (B1) Data obtained when the stimuli shown in A1 and A2 were delivered
simultaneously, showing the locations of the responses to 16 repetitions of this stimulus pairing. The average response (dashed line) is nearly half-way between the
average of the two individual responses. (B2) Directions of the responses predicted from the individual responses of the P cells. The tighter clustering of the responses
indicates that much of the variability in the responses to touch was due to the variability of the P cell responses. (C1 and C2) Responses of the model from Fig. 10 to the
same P cell activations shown in B1. These responses show the same general features as the real data, suggesting that the neuronal network, as characterized, is
essentially complete (from Lewis and Kristan, 1998a).
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site of touch. The orderly overlap in the receptive fields of the
neurons at each level provides a simple network to calculate a
population vector. Such a system has been proposed for a
variety of other behaviors: eye movements in mammals (Sparks
et al., 1997), arm movement in primates (Georgopoulos et al.,
1988), escape responses in crickets (Theunissen et al., 1996)
and cockroaches (Camhi and Levy, 1989), and flight orientation
in insects (Douglass and Strausfeld, 2000). In all these systems,
a common theme is the mapping of location from a sensory
space onto motor space to produce an accurate response with a
minimal number of neurons.
The most obvious gaps in our knowledge of the local
bending system are the ‘‘missing’’ LBIs (i.e. the ones with
ventral receptive fields, and determining how the LBIs connect
to circular muscle motor neurons). But even without this
knowledge, the neuronal circuitry for local bending is so well
characterized, that it provides a very useful system for testing
ideas about coding. Previous studies have suggested that the
location of local bending is encoded in the relative number of
impulses in adjacent P cells. This can be tested by recording
intracellularly from two P cells with adjacent receptive fields
and imposing variations in their normal firing patterns by
stimulating them in variations of their normal patterns. In other
words, the convincing way to determine the validity of a code is
to see whether the system can in fact decode the hypothesized
code. In addition, it would be useful to know the function(s) of
the inhibitory connections among the MNs. Are they simply for
lateral inhibition, or do they contribute significantly to the
calculation of the population vector? How these calculations
are performed, as well as the basis for the independence of
variations in MN responses (Zoccolan et al., 2002), can be
traced out in the activity of the LBIs.
3.3. Shortening
Shortening in the leech is caused by a co-contraction of all
longitudinal muscles in body segments. Contractions can occur
in all segments (whole-body shortening) or only a few segments
(local shortening).
3.3.1. Whole-body shortening
Moderate activation of mechanosensory neurons – particularly P cells – in the anterior end of the leech reliably produces
whole-body shortening (Fig. 3C). Such stimulation activates
two parallel interneuronal pathways: a fast, weak one and a
slower, more prolonged one (Fig. 12A). The S cells are the
central elements in the fast pathway. There is a single S cell in
each ganglion (Frank et al., 1975) that sends an axon both
rostrally and caudally into the medial connective (Faivre’s
nerve). The axons of the S cells are among the largest in the
leech CNS, and have the fastest conduction velocity. The axons
from S cells in adjacent ganglia meet midway between the
ganglia and make highly effective electrical junctions that
allow action potentials to pass without fail in either direction.
These interconnected S cells form a reliable fast-conducting
system (FCS, Bagnoli et al., 1975) that carries action potentials
from the site of spike initiation (which can be in the S cell in any
ganglion) to every ganglion in the nerve cord (Frank et al.,
1975). The S cells respond to mechanosensory and photic
inputs (Frank et al., 1975; Bagnoli et al., 1975). In each
segment, S cells make electrical connections with L cells, the
MNs that, as stated earlier, cause all longitudinal muscles in one
hemisegment to contract. The FCS is strongly activated during
whole-body shortening (Magni and Pellegrino, 1978; Mistick,
1978). In the Amazonian leech, Haementeria ghilianii, all the
connections are sufficiently effective so that activating the S
cell at moderate rates produces a significant shortening
response (Kramer, 1981). In Hirudo, however, the S-to-L
connection is so weak that stimulating the S cell even at rates
higher than those observed during shortening behavior,
produces only a very weak motor response (Shaw and Kristan,
1999). The strongest, most behaviorally relevant pathway is
from mostly unidentified INs, which have much slower
conduction velocities than the FCS, but which produce a
strong and prolonged activation of the MNs with more limited
motor fields than the L cell. It appears that the FCS provides a
fast but subthreshold activation of the longitudinal muscles,
upon which the slower system produces full-blown shortening
(Fig. 12B and C).
These interneuronal pathways activate all the excitatory
MNs to the longitudinal muscles in all segments of the body
with short latencies. The S cell excitation spreads at 5–6 ms per
segment, and the other pathways are significantly slower, at 15–
17 ms per segment (Shaw and Kristan, 1999). In addition, the
inhibitory MNs are strongly inhibited during whole-body
shortening (Shaw and Kristan, 1995). There is significant
variability in the response of individual MNs during wholebody shortening, but the ensemble average of the MN response
is much less variable, because the responses of each MN is
statistically independent from all the others (Pinato et al., 2000;
Arisi et al., 2001).
3.3.2. Local shortening
Mechanical stimulation of the skin in mid-body segments
elicits a local shortening response (Fig. 13A; Wittenberg and
Kristan, 1992a). As is true for whole-body shortening, P cells
are most effective in producing local shortening; in both
behaviors all the longitudinal muscle excitors are excited and
all the inhibitors are inhibited. What distinguishes the two types
of shortening is that local shortening is restricted to the
segments adjacent to the stimulated segment rather than
involving the whole body. The number of segments involved in
local shortening increases with increasing stimulus intensity,
until, at the extreme, the whole body can be involved. Even at
this extreme, however, local shortening differs from wholebody shortening in that the segment being stimulated produces
a bend rather than a shortening (i.e. the longitudinal muscles on
the side of the segment being stimulated contract and those on
the other side relax). The consequence of this radiating
shortening on either side of the site is that the degree of bending
about the site of stimulation increases with increasing stimulus
intensity. Interestingly, a subset of the very same INs
responsible for local bending send processes out of their
own ganglion into several adjacent ones, where they make
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Fig. 12. Neuronal bases of whole-body shortening. (A) Schematic summary diagram of what is known about the neuronal circuit underlying whole-body shortening.
Touch (T) and pressure (P) neurons in the anterior end (from the brain down to about segmental ganglion 7) activate both the fast, but weak S cell network, and a slower,
stronger network (other pathways); both drive the excitatory MNs (DE, VE, and L) to longitudinal muscles and inhibit the inhibitors (DI, VI) to these same muscles in all
segmental ganglia. (B) Synaptic input onto excitatory MNs during whole-body shortening in a semi-intact preparation (diagram on the left). Activating mechanosensory
neurons in segments 3 and 4 (stimulus) excited both the L cell in segment 11 for the duration of the stimulus train and the dorsal excitor cell DE-3 for a much longer time.
Extracellular recordings in the same ganglion showed the spikes from the L cell (marked by dots over the DP:B1 recording—branch 1 of the dorsal posterior nerve) and cell
3 (the large spikes in the DP:B2 recording). The tension recordings show that the response is somewhat larger and faster in the anterior end than in the posterior end. (C) The
whole-body nature of the shortening response. Stimulating the skin in segment 4 activates the P cell in that segment, as shown in the Pd intracellular recording from that
segment (top trace). The extracellular recordings from nerves DP:B1 and DP:B2 in segments 7 and 12 (bottom four recordings) show that the L cell and cell 3 in these
segments were also activated during the shortening response. The stimulus marker provides a time calibration, which is 500 ms long.
connections to MNs that produce shortening rather than
bending (Fig. 13B; Wittenberg and Kristan, 1992b). It will be
interesting to find out whether the two kinds of shortening share
neuronal circuitry in the individual segmental ganglia.
3.3.3. Gaps in our knowledge
For both whole-body and local shortening, there are more
gaps than knowledge. Whole-body shortening is a very
overpowering behavior; it appears to override all behaviors
but feeding (Shaw and Kristan, 1997), and it activates the L
cells, which cause the fastest and most powerful contraction of
any longitudinal MNs (Stuart, 1970; Mason and Kristan, 1982).
Some of the INs that trigger swimming are also active during
shortening (Shaw and Kristan, 1997), so it will be interesting to
determine how these very different behaviors share crucial
interneurons. In addition, the function of the two parallel
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underlying rhythmic neuronal activity patterns. Nearly concurrently, Brown (1911) proposed that central neuronal
oscillators generate these neuronal substrates via reciprocal
inhibition between neurons within the spinal cord. Ironically,
both researchers studied the same behavior – stepping – in
mammals. Despite the demonstration by Adrian that there is an
inherent rhythmicity in the CNS of the beetle Dytiscus (Adrian,
1931), Gray and coworker’s seminal work on swimming in
leeches (Gray et al., 1938), and even the demonstration
that crustacean swimmeret movements are generated by
neuronal oscillators (Ikeda and Wiersma, 1964), the chainof-reflexes view prevailed for more than 50 years, leading to
Wilson’s critical research on flight in deafferented locusts
(Wilson, 1961). Finally, research on the cardiac ganglion and
stomatogastric systems in lobster (Hartline, 1967; Mulloney
and Selverston, 1974), leech swimming (Kristan and Calabrese,
1976), and lamprey swimming (Cohen and Wallén, 1980), as
well as numerous additional preparations (Selverston, 1985;
Marder and Calabrese, 1996), clinched the argument unequivocally in favor of the central oscillator theory (Delcomyn,
1980); or so it seemed (Pearson, 2000).
Fig. 13. Neuronal bases of local shortening. (A) A single P cell located in the
middle of the animal (PD, segment 10) was stimulated intracellularly (bottom
trace). The response was measured in DP nerve recordings from segments
throughout the nerve cord. This stimulus elicited the largest response in the
same segment stimulated, with a smaller response in adjacent segments, both
anterior (9,L) and posterior (11,L). The excitation did not spread to either the
most anterior segment recorded (4,L) or the most posterior one (18,L). (B) The
response that spreads to other ganglia activates both dorsal and ventral
excitatory MNs. Stimulating a PD neuron in ganglion 8 (bottom trace) activated
cell 3, a dorsal excitor; cell 4, a ventral excitor; as well as the L cell (which
contracts both dorsal and ventral longitudinal muscles) in ganglion 10.
pathways (Fig. 12A) is provocative. It may be that the fast
pathway is to ready the animal to behave and the slower
pathway actually produces the response. As discussed in
Section 3.8.3 below, the S cell is very important for plasticity of
the shortening response. Perhaps the INs that are active in
shortening and other behaviors have a special role in plasticity.
3.4. Swimming
3.4.1. History: reflex chain versus central pattern
generator
Early studies on the neuronal bases of animal locomotion
spawned two opposing theories. The first, effectively promulgated by Philippson (Philippson, 1905, cited in Gray, 1950) and
Sherrington (1906) at the beginning of the 20th century, was
that coordinated chains of sensory reflexes generate the
3.4.2. Swimming behavior and motor control
This review of leech swimming begins with a brief overview
of the circuits and mechanisms by which muscles, MNs,
sensory neurons and INs control leech swimming movements.
The older results have been reviewed many times (Stent et al.,
1978; Kristan, 1974, 1980, 1983; Kristan and Weeks, 1983;
Friesen, 1989d; Brodfuehrer et al., 1995b). Thus weight is
given to recent experiments on the role of sensory feedback in
setting intersegmental phase lags and cycle period, and an
examination of functional aspects of the central oscillator and
intersegmental coordination.
The quasi-sinusoidal undulations that characterize swimming leeches (Fig. 14A) are a consequence of tension and
relaxation cycles in two types of segmental muscles. First, the
leech body, is flattened to form an elongated ribbon by tonic
contractions of dorsoventral muscles. Second, the alternating
contraction and relaxation of dorsal and ventral longitudinal
muscles (DLM and VLM, respectively) act against internal
pressures, with a period of about 0.3–1.0 s, to generate
rhythmic bending in body segments (Fig. 14B; Uexküll, 1905;
Gray et al., 1938; Kristan et al., 1974a; Wilson et al., 1996b).
Two major technical advances were critical for research into the
neurobiological substrates of leech swimming movements: one
was the advent of extracellular recordings, the other the
development of a semi-intact preparation in which neuronal
activity and body wall movements are available simultaneously
for detailed analyses (Gray et al., 1938). These techniques,
together with intracellular recording, revealed that the
antiphasic contraction and relaxation of the DLM and VLM
are commanded, respectively, by excitatory (six pairs/segment)
and inhibitory (five pairs/segment) motoneurons (MNs; Stuart,
1970; Ort et al., 1974; Norris and Calabrese, 1987).
Intersegmental phase lags of approximately 208/segment in
MN activity ensure the generation of the rearward-traveling
body wave (Kristan et al., 1974a,b). These MNs are
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Fig. 14. Expression of leech swimming movements. (A) Swimming leeches generate a quasi-sinusoidal wave of approximately one full wavelength. These swim
profiles are essentially identical in three different leeches of very different length and weight. (B) Swimming movements result from antiphasic contractions in dorsal
and ventral longitudinal muscles. The inset depicts the nerve cord–body wall preparation used to obtain the extracellular records [DP(R,7) and DP(R,14)] and the
tension measurements (dorsal and ventral tension, obtained from the proximal ventral and the distal dorsal body wall strips). A single swim episode was elicited by
nerve stimulation (artifacts in the DP nerve traces). The inset at the bottom shows the four traces at higher temporal resolution to illustrate the intersegmental phase
lags of MN activity and the antiphasic tension rhythms in the muscles.
interconnected by inhibitory chemical synapses from the dorsal
inhibitor (DI) to dorsal excitatory (DE) MNs, and from ventral
inhibitor (VI) to ventral excitor (VE) MNs (Ort et al., 1974;
Granzow and Kristan, 1986). The DIs also inhibit the VIs, an
interaction that, unlike most other MNs, crosses the ganglion
midline (Friesen, 1989a).
Once thought to be an exception to the rule that rhythmic
movements in animals are generated by neuronal oscillators
located within the CNS (Kristan and Stent, 1975), further
research revealed that isolated nerve cord preparations
(comprising eight or more of the 21 midbody ganglia) are
capable of generating rhythmic bursting in MNs that is an
excellent facsimile of the overtly expressed rhythm (Kristan
and Calabrese, 1976). This fictive swimming has a longer cycle
period (0.5–2 s) and reduced intersegmental phase lags (about
108/segment) from that seen in intact animals (Pearce and
Friesen, 1984). Experiments on semi-intact preparations also
revealed that, although sensory feedback is not necessary for
generating the basic swim rhythm, feedback loops, perhaps
acting via the DI, are indeed present (Kristan and Calabrese,
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1976). These seminal results laid a solid foundation for studies
of the central oscillator that generates the swimming rhythm,
including studies on the central and sensory mechanisms that
control initiation and termination of swim episodes, the
mechanisms for chemical modulation of swim expression,
and most recently, the role of sensory feedback in intersegmental coordination.
3.4.3. Central oscillator circuits
The swimming rhythm is not generated by a MN circuit.
Although MNs are phasically active and interact with each
other, this network is not sufficiently complex, nor do MNs
have intrinsic oscillatory properties, to generate the swim
oscillations. Subsequent searches among the considerably
smaller INs identified thirteen candidate swim oscillator INs in
most midbody ganglia (at least from M2 to M16) by the criteria
that: (a) their membrane potential is phase-locked to the
swimming rhythms, (b) current injection into the IN somata –
either depolarization or hyperpolarization – shifts the phase of
swimming activity, and (c) there are synaptic interactions with
other candidate oscillator INs (Friesen et al., 1976, 1978;
Weeks, 1982b; Friesen, 1985, 1989b). A set of INs that satisfies
all three criteria is found on both the dorsal and ventral aspects
of the midbody ganglia (Fig. 15). With somata ranging from
about 10–15 mm in diameter, these INs are inhibitory and
bilaterally paired (cells 27, 28, 33, 60, 115, and 123), except for
an unpaired medium-sized excitatory IN (cell 208). Without
exception, their axons project either anteriorly or posteriorly in
lateral nerve cord connectives (Poon et al., 1978; Weeks,
1982b; Friesen and Hocker, 2001). However, definitive
information about maximal projections for individual oscillator
INs remains elusive. The current understanding is that INs
project about six segments, but not more than seven segments
(Pearce and Friesen, 1985b; Friesen and Hocker, 2001), except
for cell 208, which may project as far as 10 segments (Weeks,
1982b).
An important characteristic of swim-related neurons is the
phase of their activity (Fig. 15B). The phase reference mark, 08,
for each segment is the median impulse in DE-3 MN bursts
recorded with extracellular electrodes (Kristan et al., 1974a).
The activity phases of other MNs are either 08 (DE and VI) or
1808 (VE and DI), except for DI-1, which has a phase of 1208
(Ort et al., 1974; Friesen, 1989d). Phases of the INs are more
dispersed, spanning 08 to nearly 3008. For simplicity and as an
aid to modeling, IN phases may be placed into three phase
groups: about 08 (40–508), 1208 (130–1708), and 2408 (220–
2608) (Friesen, 1989d). Intersegmental phase lags in the
isolated nerve cord preparations are 8–108 for extended
preparations (Kristan and Calabrese, 1976), but increase to
about 408 if preparations are reduced to two segments (Pearce
and Friesen, 1985b).
Nearly all pairs of bilaterally homologous neurons in the
leech swim circuit are electrically coupled. More importantly,
the swim-related oscillator INs are interconnected via
inhibitory synapses (Fig. 15A). Only one set of these IN
interactions (between cells 27 and 28) is strictly reciprocal;
other interactions appear reciprocal if laterality of INs is
ignored (Fig. 15B). Reciprocal interactions, both direct and via
electrical coupling, also link the DI and VI MNs to the INs.
Because of these interactions and because their depolarization
can shift the phase of swimming activity, these inhibitory MNs
are candidate members of the swim oscillator circuit (Kristan
and Calabrese, 1976; Friesen, 1989b; Mangan et al., 1994b).
The unpaired cell 208 makes only excitatory connections
(Weeks, 1982b; Nusbaum et al., 1987). Tests to determine
whether intrasegmental interactions in the swim circuits are
monosynaptic have proven difficult, in large part because many
of these interactions are not spike-mediated. Consequently,
individual synaptic potentials arising from synaptic connections within ganglia are rarely observed and action potentials
are not required for synaptic interactions (Granzow et al., 1985;
Friesen, 1985). Intersegmental interactions, however, occur via
spike-mediated synapses. Those connections made by rostrally
projecting INs are repeats their intrasegmental connections,
whereas synaptic outputs by caudally projecting neurons are
more diverse (Fig. 15C). Not shown in Fig. 15 are the very
extensive intra- and intersegmental connections by which the
INs and inhibitory MNs control the excitatory MNs (Poon et al.,
1978; Weeks, 1982b). For completeness, it is essential to point
out that inhibitory inputs to cells 60 and 33 are unknown and to
mention that two additional neurons, cell 18 (Nusbaum, 1986)
and 42 (Poon, 1976), are candidate, but poorly characterized,
oscillator neurons. (Discussion of the mechanisms that generate
swim oscillations is found below, in Section 3.4.7).
3.4.4. Control of swimming activity
Leech swimming activity is largely an episodic, rather than
continuous, behavior, whether evoked in the intact animal, in
semi-intact preparations, or in preparations of the isolated leech
cord. In intact animals, swimming may be initiated by a variety
of sensory inputs including tactile stimulation of the body wall
and water movements. In the isolated nerve cord, bouts of
fictive swimming are readily obtained in response to
stimulation of a peripheral nerve, intracellular stimulation of
single neurons, or even without stimulation, e.g. as ‘spontaneous’ episodic events following application of serotonin (5HT) or other neuromodulators. The neuronal network consists
of at least three layers in addition to the two (oscillator INs and
MNs) that have already been considered (Fig. 16). This section
discusses the roles of the neurons in each of these hierarchical
layers.
Water vibrations, such as those caused by surface waves or
due to water currents, also reliably elicit swimming in hungry,
quiescent leeches (Young et al., 1981). In addition, these
surface waves provide directional cues causing aroused leeches
to swim towards the source of such waves. The anatomical
structures responsible for these responses are sensillar movement receptors (SMRs), sensory hairs that cluster at the seven
pairs of sensilla located on each midbody segment (DeRosa and
Friesen, 1981; Phillips and Friesen, 1982). In scanning electron
microscopy (SEM), SMRs appear as 1 mm wide, 10 mm long,
single hairs; in transmission EM, SMRs have the distinct 7 + 2
microtubule structure of sensory hair cells. Physiologically,
responses recorded from individual sensillar nerves are
W.B. Kristan Jr. et al. / Progress in Neurobiology 76 (2005) 279–327
301
Fig. 15. Intra- and intersegmental interactions between swim-related oscillator INs and MNs. DI and VI are inhibitory MNs; 123, 115, 60, 33, 28, and 27 are
inhibitory INs; and 208 is an excitatory IN. (A) Subset of the neurons showing bilateral and ipsilateral interactions. Numerous reciprocal inhibitory interactions occur
between INs and MNs across the midline of individual ganglia. (B) The segmental swim oscillator circuit with laterality of neuronal interactions collapsed. (C)
Intersegmental interactions between the INs (gray lines denote intrasegmental connections). In B and C neurons are arranged in columns reflecting three phase
groupings (top of B). Filled circles, inhibition; T-endings, excitation; resistor, nonrectifying electrical interaction; diode, rectifying electrical interaction. Both the
synaptic targets and the sources of synaptic inputs for some neurons remain unidentified. (Modified from Friesen and Hocker, 2001, Fig. 1).
selective to near field stimulation and appear in extracellular
recordings from sensory nerves as graded compound action
potentials that cover a wide frequency range [about 0.1–10 Hz
(Friesen, 1981; Brodfuehrer and Friesen, 1984)]. Target cells
for water vibration stimulation include the CBW cells found in
the anterior medial packet on the ventral side of many midbody
ganglia (Gascoigne and McVean, 1991).
Sensory stimulation initiates swimming activity through a
cascade of interactions that eventually drive the swim oscillator
(Fig. 16). One pathway in this cascade comprises the P and N
sensory neurons, which drive trigger neurons (Tr1 and Tr2) via
monosynaptic connections (Brodfuehrer and Friesen, 1986c,e).
The somata of trigger neurons are located in the subesophageal
ganglion; their axons project from that origin into the caudal
nerve cord, with broadly distributed input and output sites in the
intervening ganglia (Brodfuehrer and Friesen, 1986a,c). Brief
stimulation of individual trigger neurons elicits bouts of
swimming activity, with no correlation between the duration of
trigger neuron activity and the ensuing swim episode
(Brodfuehrer and Friesen, 1986a). Trigger neuron Tr2 is
particularly interesting in that it acts as a toggle switch: brief
stimulation can elicit swimming activity and a second stimulus,
once swimming has commenced, brings that activity to an
abrupt halt (O’Gara and Friesen, 1995). Multiple, as yet
unknown factors, determine whether swimming will occur in
response to any stimulus (Cellucci et al., 2000). Trigger
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Fig. 16. Cascade of information flow from mechanosensory neurons to the
swim-related muscles. Cell names: T, touch cell; P, pressure cell; N, nociceptive
cells; SMR, sensillar movement receptor; Tr1 and Tr2, trigger neurons 1 and 2;
SE1, swim excitor neuron 1; SIN1, swim inhibitor neuron 1; VE and VI, ventral
excitors and inhibitors; DE and DI, dorsal excitors and inhibitors. FL, flattener
neuron. The small inset numbers designate the reference phase of each
oscillatory neuron. Darkly filled circles designate inhibitory neurons or those
that inhibit swimming. The synaptic targets of some cells are unidentified.
Synaptic symbols as in Fig. 15.
neurons, which receive no feedback from the swim oscillator
neurons, may be regarded as the highest centers for the control
of swimming activity (Friesen, 1989c,d). Segmental targets of
Tr2 have been found (Taylor et al., 2003); these neurons slow or
stop ongoing swim motor patterns. Also located in the
subesophageal ganglion, a pair of swim inhibitory INs (SIN)
may be part of the swim-inactivating system (Brodfuehrer and
Burns, 1995), whereas another set of neurons, the excitatory
SE1 cells, may act as gain control elements that determine
whether swimming will occur in response to some specific
stimulus (Fig. 16; Brodfuehrer et al., 1995a). The subesophageal ganglion may also contribute directly to swim oscillations
via cell SRN1, a brain IN that exhibits oscillations phase-locked
to the swimming rhythm and which can shift the swimming
phase (Brodfuehrer and Friesen, 1986d).
Gating neurons, such as cell 204 and its homolog, cell 205
(Weeks, 1982a,c) occupy the third level of the swim-initiation
cascade. The somata of these unpaired excitatory INs are
restricted to the posterior half of the nerve cord (M9–M16). Their
axons project to most, if not all ganglia of the nerve cord. Strong
depolarization of even an individual swim gating neuron drives
the expression of swimming activity even in nearly isolated
ganglia (Weeks, 1981); when repeatedly activated, swim
duration does not outlast the depolarization. When swimming
activity is elicited by any means, including brief trigger neuron
activity, these cells depolarize and remain depolarized throughout the swim episode (Weeks and Kristan, 1978). Part of this
excitation results from monosynaptic excitatory input from
trigger neurons (Brodfuehrer and Friesen, 1986b), but the source
of the persistent depolarization remains unidentified.
Body wall stimulation, followed by Tr1 activation, elicits
concurrent depolarization of all cell 204 gating neurons. These,
in turn, provide nearly simultaneous excitatory drive to a subset
of the oscillator INs throughout the nerve cord. (Tr2 interactions
with gating cells are polysynaptic and inhibitory.) Cells 204/205,
in turn, drive both INs and MNs (Weeks, 1982a,c; Nusbaum et al.,
1987). Interestingly, only three identified members of the central
swim oscillator receive direct input from cells 204/205: cells 115,
28, and 208. At present, the only identified target for Tr2 is cell
256, a neuron that terminates swimming activity (Taylor et al.,
2003). The neurotransmitter acting at the top of the excitatory
cascade is glutamate, acting through non-NMDA receptors at
synapses between P cells and Tr1, between Tr1 and cell 204, and
between cell 204 and cells 28, 115, and 208 (Brodfuehrer and
Cohen, 1990, 1992; Thorogood and Brodfuehrer, 1995;
Brodfuehrer and Thorogood, 2001). The great importance of
gating neurons 204/5 in controlling swim period may be deduced
from the fact that impulse frequency in these neurons, whether set
by the experimenter or observed during swim episodes, predicts
swim period with high precision (Pearce and Friesen, 1985a;
Debski and Friesen, 1986). Flattening of the leech prior to
swimming is mediated via activation of flattener MNs by cells
204, 205, and 208 (Weeks, 1982a,b,c).
A set of serotonin-containing neurons, cells 21/61, also gate
swimming (Nusbaum, 1986; Nusbaum and Kristan, 1986).
These neurons receive input from T, P and N cells indirectly
(Gilchrist and Mesce, 1997) and make excitatory interactions
with the same oscillator INs as cells 204/5. Despite these
similarities to cells 204/5, these more locally acting neurons are
less robust in their swim initiation abilities, at least in Hirudo
(Nusbaum et al., 1987).
Control of leech swimming is obviously more complex than
suggested by the linear cascade outlined in Fig. 16. For
example, swimming activity evoked in ventral nerve cords that
include the head ganglia is weaker and less regular than in
brainless preparations, particularly in bursting recorded in the
anterior half of the nerve cord (Brodfuehrer and Friesen,
1986d). Thus, the head ganglia have a marked inhibitory effect
on swimming. In contrast, the tail ganglion facilitates swim
initiation and duration, by reversing the inhibitory action of the
head ganglia and prolonging swim episodes (Brodfuehrer et al.,
1993). These differing effects of the head and tail brains initiate
and reverse within seconds when impulse traffic to the brains is
interrupted and reinstated, hence synaptic rather than hormonal
mechanisms appear to be at work here, although those that
mediate the excitatory actions of the tail ganglion are unknown.
3.4.5. Neuromodulatory control: serotonin and other
biogenic amines
Although neurons in the leech subesophageal ganglion exert
the highest level of control over swim initiation and
W.B. Kristan Jr. et al. / Progress in Neurobiology 76 (2005) 279–327
termination, the overall propensity for swimming is regulated
by neuroactive substances, most notably by 5-HT. Leeches
with a high blood concentration of 5-HT swim more, and
isolated nerve cord preparations engage in swimming activity
spontaneously, when 5-HT levels are elevated (Willard,
1981). Swimming activity reaches a half-maximal level about
15 min after exposure to 50 mM 5-HT and returns to control
levels about 30 min after washout. The primary effect in the
isolated nerve cord (without the brain) is the appearance of
spontaneous swim episodes, with few if any changes in cycle
period, impulse frequencies, and the duration of episodes
(Hashemzadeh-Gargari and Friesen, 1989). Bath application
of 5-HT can even induce isolated ganglia individual to
express the rudiments of the swimming rhythm. These
hormonal effects, also observed when the 5-HT-containing
Retzius cells are stimulated, stand in contrast to the rapid
activation of swimming by depolarization of the other 5-HTcontaining neurons, cells 21/61 (Kristan and Nusbaum, 1983).
In the latter case, swim initiation appears as a synaptic rather
than as a hormonal event; however, the importance of 5-HT,
acting as a local hormone and neuromodulator, cannot be
overstated. There is a negative correlation between the state of
satiety in leeches and swimming behavior—hungry leeches
have higher levels of 5-HT and swim more readily in response
to stimulation (Young et al., 1981; Lent, 1985). Therefore it
appears likely that 5-HT levels are broadly important in
regulating the appetitive phase of feeding behavior in the
leech (Lent and Dickinson, 1984).
Because focal application of 5-HT to the subesophageal
ganglion terminates swimming (Crisp and Mesce, 2003),
spatial differences in 5-HT modulation may contribute to
transitions from the appetitive phase of feeding (swimming) to
the consummatory one (ingesting the blood meal), when
swimming is counterproductive. During the initiation of
ingestion, the serotonergic Retzius neurons stop firing (Wilson
et al., 1996; Zhang et al., 2000), in sharp contrast to an earlier
view in which the Retzius neurons were thought to command
both phases of feeding (Lent, 1985). Although the behavioral
roles of dopamine are the least understood of the amines,
dopamine also may play an important role in feeding (biting)
behavior because dopamine-containing fibers have been found
within the three accessory ganglia controlling the jaws (Crisp
et al., 2002). In addition, bath application of dopamine to
isolated nerve cords inhibits all ongoing and nerve-evoked
swimming; this is correlated with the discovery that
dopaminergic neurons are directly coupled to Tr2 (Crisp and
Mesce, 2004). Thus dopamine has the potential to coordinate
biting with the termination of swimming. [As mentioned
earlier, Tr2 has been found to be a more potent swim terminator
than swim activator (O’Gara and Friesen, 1995).] Octopamine,
the last of the monoamines discussed here, can induce
swimming when bath applied to isolated nerve cords with
the brain removed (Hashemzadeh-Gargari and Friesen, 1989)
or with it attached (Crisp and Mesce, 2003). The dorsolateral
octopamine (DLO) cells have been identified as the set of
segmental neurons containing and synthesizing octopamine
(Gilchrist et al., 1995; Crisp et al., 2002). The segmental Leydig
303
cells, which closely neighbor the DLOS, were at first thought to
contain octopamine (Belanger and Orchard, 1988). Although
the Leydig cells are not octopaminergic or synaptically linked
to the DLOs (Gilchrist et al., 1995; Crisp et al., 2002), these
earlier studies helped to establish that octopamine is a naturally
occurring neuromodulator in the leech. Because swimactivating mechanosensory inputs (T and P cells) have been
found to co-activate the DLOs, these octopamine and 5-HTcontaining cells (Gilchrist and Mesce, 1997) may act in parallel
with the Retzius neurons and other serotonergic cells (61 and
21). Subsequent studies have documented that bath application
of a mixture of the two amines (isolated nerve cords with
brains) results in a novel non-additive suppression of
swimming, followed by a robust activation of swimming after
the mixture is removed during a 30 min saline wash (Mesce
et al., 2001). Related to the modulation of feeding behavior, 5HT or octopamine also might be involved in the circadian
regulation of behavior observed in a predatory leech (Angstadt
and Moore, 1997).
Further evidence that 5-HT is a critical hormone for the
expression of swimming activity comes from depletion
studies. Thus in isolated nerve cords acute treatment with
reserpine – which blocks the monoamine vesicle transporter –
eliminates all swimming activity, which is restored when 5-HT
is subsequently bath-applied (Hashemzadeh-Gargari and
Friesen, 1989). In intact animals, injection of reserpine into
the crop depletes all amines for many weeks. Such animals
cease normal biting behavior, their bodies become rigid, and
sensitivity to stimulation is greatly reduced. Surprisingly,
prolonged tactile stimulation can evoke swimming, which then
persists far longer than in control animals (O’Gara et al., 1991).
This apparent anomaly may be a consequence of the loss
of other amines, such as dopamine or octopamine, which are
also depleted by reserpine (O’Gara et al., 1991). Because
dopamine has now been linked to the suppression of swimming
(Crisp and Mesce, 2004), the reserpine-induced loss of this
inhibitory factor could contribute to prolonged swimming in
treated animals. Finally, it was found that when 5-HT is
chronically removed from the nervous system of juveniles
through treatment with 5,7-dihydroxytryptamine (5,7-DHT),
swimming is not expressed in the adults (Glover and Kramer,
1982).
The swim gating neurons, cells 204/5, with their direct
involvement in swim initiation, are obvious candidates for
modulation by 5-HT. A comparative study of cell 204
properties in normal saline and following exposure to bathapplied 5-HT revealed that 5-HT reduces the threshold for swim
initiation via intracellular depolarization of these swim-gating
neurons. Moreover, elevated 5-HT converted cell 204 into a
trigger cell, so that even brief stimulation of cell 204 elicited
swim episodes (Fig. 17A and B; Angstadt and Friesen, 1993a).
Concomitant with these functional alterations, cell 204 in the
presence of bath-applied 5-HT displayed enhanced postinhibitory rebound and afterhyperpolarization, mediated by Na+dependent and Na+-independent conductances (Angstadt and
Friesen, 1993b). The DI and VI MNs were depolarized slightly
by elevated 5-HT levels but, as for cell 204, steady state I–V
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importantly from a functional viewpoint, brief pulses of
injected current caused larger phase shifts when the inhibitory
MNs were exposed to bath-applied 5-HT. The dynamics of the
synaptic interactions of these MNs were altered by 5-HT, with
increased and more rapid onset of, and recovery from, synaptic
fatigue (Fig. 17C; Mangan et al., 1994b). Application of drugs
that increase the intracellular concentration of cAMP mimic
the effects of bath-applied 5-HT (Hashemzadeh-Gargari
and Friesen, 1989), hence elevated cAMP is a likely mediator
of the swim enhancing effects of 5-HT, whether through
the alterations demonstrated in cell 204 and the inhibitory
MNs, in oscillator INs, or in as yet unidentified swim excitor
cells.
Fig. 17. Modification of neuronal physiology and function by 5-HT. (A and B)
Bath application of 5-HT transforms the swim-gating neuron, cell 204, into a
trigger cell in an isolated nerve cord preparation. In both panels, the upper traces
show the amplitude and duration of intracellular currents applied to cell 204
(middle traces). Identical current pulses have little effect in normal saline (A),
but elicit a swim episode when 5-HT is elevated (DP records in lower traces of
B). (C) Serotonin induces time-dependent membrane potential trajectories in
MNs. Depolarization (by current injection, not shown) of an inhibitor MN (cell
DI, upper traces) hyperpolarizes its postsynaptic target (the contralateral cell
VI, lower traces). Bath-applied 5-HT induces presynaptic relaxation and
postsynaptic fatigue with subsequent postinhibitory rebound (parts A and B
are reprinted with permission from Angstadt and Friesen, 1993a; part C is
modified from Mangan et al., 1994a,b).
relationships were essentially unaltered (Mangan et al., 1994a).
As observed in cell 204, 5-HT induced an enhancement of
postinhibitory rebound and of afterhyperpolarization following the cessation of depolarizing current pulses. Most
3.4.6. Role of sensory feedback
In addition to the mechanosensory activity that elicits
swimming, the neuronal circuit for swimming is also affected
by sensory feedback during the production of swimming.
Recently, the source of this sensory feedback has been
described definitively as stretch receptors in the leech body
wall, and the detailed function of one type of stretch receptor –
those located in the ventral body wall – has been described in
detail.
Whole-body undulations, as seen during swimming in
leeches, lamprey, or snakes is most effective when the
waveform comprises about one wavelength (or slightly greater
to minimize yaw; Fig. 14A; Taylor, 1952; Gray, 1958, 1968;
Kristan et al., 1974a). The triumphant central oscillator theory
informs us that neuronal circuits within the CNS generate the
fundamental rhythms, however caveats reminding us of the
importance of sensory feedback for shaping fully expressed
movement rhythms have appeared repeatedly in the literature
on motor systems (Kristan and Stent, 1975; Kristan and
Calabrese, 1976; Pearson et al., 1983; Pearson and Ramirez,
1990). In the leech, sensory feedback plays a significant role in
setting the swim cycle period, which is longer in the absence of
sensory feedback (Kristan and Calabrese, 1976). More
critically, intersegmental phase lags, which are about 208/
segment in swimming leeches are reduced to 8–108/segment in
the isolated nerve cord (Pearce and Friesen, 1984). Feedback
appears to be less critical in the expression of swimming
behavior in lampreys and in coordination within the crayfish
swimmeret system, where the isolated CNS can generate
appropriate periods and intersegmental phase lags (Wallén and
Williams, 1984; Grillner et al., 1991; Mulloney et al., 1993;
Friesen and Cang, 2001).
Sensory feedback is, of course, a feature of all animal
locomotory systems. In leeches, feedback was deduced from
experiments with the following results: (1) the cycle period of
swimming leeches increases when the viscosity of the medium
is increased (Gray et al., 1938); (2) stretching the body wall
in nerve cord - body wall preparations alters the intensity of
MN bursts, increases cycle period, and can even terminate
swimming activity (Kristan and Calabrese, 1976); (3)
mechanical expression of body movements is essential for
the continuation of swimming movements in a partially
restrained leech (Kristan and Stent, 1975) and (4) interseg-
W.B. Kristan Jr. et al. / Progress in Neurobiology 76 (2005) 279–327
mental coordination between two ends of a leech with a severed
nerve cord is largely unimpaired, albeit there are greater
intersegmental phase lags across the lesion site (Fig. 18A–C;
Yu et al., 1999). This last set of experiments overturned the
long-held view that an intact nerve cord is essential for
intersegmental coordination in leech swimming (Schülter,
1933; Kristan et al., 1974a). Moreover, because intersegmental
phase lags generated within the nerve cord are too small, and
those engendered by sensory feedback alone are too large, these
experiments suggest that the shape of a swimming leech, a
single cycle of a quasi-sinusoidal wave, arises from a
compromise between central and peripheral coordination (Yu
et al., 1999).
Eight pairs of putative stretch receptors embedded in the
segmental longitudinal muscles of leech body walls appear to
have properties required for providing appropriate sensory
feedback during swimming. These sensory neurons have
peripheral somata, with dendrites that insert into longitudinal
muscle fibers (Blackshaw and Thompson, 1988; Blackshaw,
1993; Huang et al., 1998). These neurons respond to stretch
with graded hyperpolarization, which is conveyed electronically to nerve cord ganglia via huge (>20 mm in diameter;
Cang et al., 2001; Gerta Fleissner, personal communication)
non-spiking axons. Recordings from the axon of the ventral
stretch receptor (VSR) – which innervates VLM – in nerve
cord—body wall preparations reveals membrane potential
oscillations during swim activity with amplitudes up to 10 mV
Fig. 18. Comparison of intersegmental phase lags in normal and ‘severed nerve
cord’ (SNC) leeches. Inset: preparations and electrode placements for intact (A)
and SNC leeches (B). Traces showing swim bursts were obtained with extracellular nerve recordings from DP(7) and DP(14). Bursting activity remains
coordinated in the two halves of the leech even with the nerve cord cut. C.
Intersegmental phase lags in three types of preparations depicted as polar plots.
Numbers are means + S.D. Phase lags in intact animals lie between those
observed in isolated nerve cords and SNC leeches. (Modified from Yu et al.,
1999).
305
and a phase of about 1408. The VSR hyperpolarizes not only
when tonic tension is increased by manually stretching the body
wall, but also when body wall tension is increased naturally via
MN (cell VE-4) stimulation (Fig. 19A–C; Cang et al., 2001).
Finally, hyperpolarization of the VSR mimics the effects of
stretching the ventral body wall on impulse rates in MNs
(Blackshaw and Kristan, 1990). These experiments demonstrate that the ventral stretch receptors are tension transducers
for longitudinal muscles. [Tactile receptors in the nerve cord
sheath might also contribute to sensory feedback (Smith and
Page, 1974)].
Recent experiments reveal that the state of the VSR can
influence intersegmental phase relationships (Cang and
Friesen, 2000). In these experiments, sinusoidal currents were
injected into the VSR axon near a midbody ganglion of an
isolated nerve cord preparation. Records of swimming activity,
and hence phase, were obtained from dorsal posterior (DP)
nerves of this midbody ganglion and of the two adjacent ones.
The phase of the sinusoidal current was varied over the full 3608
of the swim cycle. Under these conditions, intersegmental
phase lags between the ganglion of the stimulated VSR axon
(ganglion n) and its nearest neighbors depended on the phase of
the injected current, such that at one phase (1208) the
n (n 1) phase lags increased by about 58 and the
n (n + 1) phase lag deceased by a similar amount.
Conversely, when the phase of injected current was set to
2708, the n (n 1) phase lags decreased by 58, and that of
n (n + 1) increased by a similar amount (Cang and Friesen,
2000). Thus, VSR activity can alter intersegmental phases in a
phase-dependent manner, and hence may be critical for setting
intersegmental phase lags to generate a single body wave.
How is stretch receptor output conveyed to the neurons of
the swim circuit? Extensive surveys with pairwise intracellular
recordings revealed only weak, presumed polysynaptic interactions between the VSR and either inhibitory or excitatory
MNs in the swim system. There is, however, a very strong
electrical connection between the VSR and cell 33. In addition,
there are inhibitory and excitatory interactions with other
oscillatory INs, cells 28, 115, and 208, that are likely to be
polysynaptic (Fig. 19D; Cang et al., 2001). VSR interactions
with INS were functionally significant, for depolarization of the
VSR greatly reduced the amplitude of swim oscillations in cells
28, 115, and 208. Also, in experiments to mimic body wall
stretch, induced VSR hyperpolarization decreased the excitatory excursions in cell DE-3 and increased those of VE-4 during
swimming activity expressed in isolated nerve cord preparations. The functional significance of VSR input for swim
oscillations is clear: (1) the VSRs undergo rhythmic oscillations
in membrane potential in preparations with the nerve cord
attached to a flap of body wall; (2) rhythmic current injection
into the VSR entrains the ongoing swimming rhythm in isolated
nerve cord preparations, with a phase angle that is positively
related to the frequency of current injection; (3) strong
depolarization of the VSR shifts the swim phase (Yu and
Friesen, 2004). These effects of the VSR on swim expression
must be mediated by interactions between the VSR and
oscillatory INs, which then provide the pathway for effects on
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the MNs, including the observed modification of intersegmental phase lags.
Of the eight putative stretch receptors in midbody segments,
an additional six have been identified through filling their axons
with Alexa Fluor1 hydrazide dye (Fan and Friesen, 2005). Two
of these have striking axon arborizations within segmental
ganglia because their processes send terminals, unlike those of
the VSR, also into the contralateral neuropil. Like the axon of
the VSR, the giant axons of these cells do not conduct action
potentials. Nevertheless, they also exhibit small, spike-like
events that are generated within their central terminals. One of
these axons is associated with dorsal longitudinal muscles and
hence is called the dorsal stretch receptor (DSR). During fictive
swimming, the DSR undergoes rhythmic oscillations that differ
in phase from oscillations recorded during swimming activity
in a nerve cord–body wall preparation. The interactions of the
DSR and the other identified putative stretch receptors with the
circuits that control swimming or other behaviors remain to be
found.
Fig. 19. Responses of the ventral stretch receptor (VSR) in a nerve cord–body
wall preparation (inset). (A) Depolarization of cell VE-4 by current injection
(upper trace, intracellular record) causes increased tension in VLM (lower
trace) that in turn induces hyperpolarization in the VSR (middle trace, intracellular record). (B) Relationships between MN activity and tension in ventral
longitudinal muscle [tension (R,11)]. Rhythmic MN activity in cell 8 (intracellular record, upper trace; extracellular record from a DP nerve, bottom trace),
causes rhythmic tension alterations in the body wall (middle trace). (C) VSR
membrane potential oscillations (upper trace) are phase-locked to the swim-
3.4.7. Functional aspects of the central oscillator
Questions about the origins of the oscillations that underlie
leech swimming have given rise to a series of answers. One
answer is that individual ganglia are capable of generating the
rudiments of the swimming rhythm when either swim-gating
neurons are stimulated (Weeks, 1981) or 5-HT is bath applied
(Hashemzadeh-Gargari and Friesen, 1989). Neuronal circuits
within these ganglia comprise a unitary oscillator rather than
the paired half-centers of the vertebrate spinal cord (Brown,
1911; Friesen and Hocker, 2001), probably because of the
extensive connections, electrical and synaptic, that link
bilateral cells in leech ganglia (Fig. 15A). Although initial
modeling of the segmental swim circuit yielded cycle periods
that were unrealistically short, more recent studies with analog
neuromimes support the view that the identified interactions
could account for these rudimentary oscillations (Wolpert and
Friesen, 2000; Wolpert et al., 2000). In fact, although highly
sensitive to parameter alterations, a computer model based on
the identified intrasegmental connections generated membrane
potential waveforms mimicking those observed in extended
nerve cords (Taylor et al., 2000).
The ability of individual ganglia to generate swim-like
oscillations is not uniformly distributed along the nerve cord.
The strongest swimming activity, either in isolated ganglia, or
pairs of ganglia, occurs in the middle third of the nerve cord,
from about M7–M12 (Pearce and Friesen, 1985a; Hocker et al.,
2000), although this activity is never as robust as that of nerve
cords extending from M2 to the tail brain. Only weak, erratic
swim-like bursting occurs in individual isolated anterior
ganglia, M2–M5; isolated ganglia posterior to M12 appear
unable to generate even such swim rudiments. For producing
swim oscillations, the functionality of nerve cord ganglia
ming rhythm (lower trace). (D) Summary of interactions between the VSR and
swim-related neurons. Of the several interactions between the VSR and cell 33,
only the electronic interaction between the VSR and cell 33 is likely to be
monosynaptic. Symbols as in previous figures (redrawn from Cang and Friesen,
2000 and Cang et al., 2001).
W.B. Kristan Jr. et al. / Progress in Neurobiology 76 (2005) 279–327
appears like an inverted U, low at the two ends and highest in
the center. In addition, the period of swimming expressed in
completely isolated ganglia, or short chains, is U-shaped; the
cycle period is shortest in the center of the cord and longer at
either end (Hocker et al., 2000). Based on the weakness of
oscillations generated in isolated ganglia, the oscillator that
generates swim oscillations should not be viewed as a chain of
weakly coupled oscillators. Instead, the system relies on strong,
extensive intersegmental interconnections to create robust
oscillations from weak subunits. As stated earlier, the INs
project at least six segments in each direction (Poon et al., 1978;
Weeks, 1982b; Pearce and Friesen, 1985b; Friesen and Hocker,
2001). Thus in the mid nerve cord, 13 oscillator subunits are
connected by direct synaptic interactions – these might be
considered to be the ‘‘unit oscillator’’. As described above,
sensory feedback contributes to intersegmental phase and to
cycle period. This is shown most dramatically in reduced
caudal preparations of the leech, which undergo vigorous,
coordinated swimming movements even though the isolated
posterior cord does not support swim oscillations (Hocker et al.,
2000). However, even single segments, if isolated chronically
from the nerve cord, can acquire swimming activity (Kristan
and Guthrie, 1977).
Intersegmental phase relationships are determined by period
gradients in unit oscillators, coupling strength, and asymmetry
in intersegmental interactions (Skinner and Mulloney, 1998a).
Asymmetry in the functional strength of intersegmental
coupling was assessed in the leech through experiments on
‘Z-cut’ preparations, in which a given midbody ganglion was
driven by input of differing cycle periods through ascending
connections on one side and descending on the other by
selective hemi-lesions of the ventral nerve cord. Correlation
and spectral density analyses of the rhythmicity in this
preparation demonstrated that effectiveness of ascending and
descending interactions in the middle of the nerve cord are
about equal (Friesen and Hocker, 2001).
The anterior-to-posterior phase lags observed during
swimming in the leech nerve cord must arise from asymmetry
in the synaptic interactions between INs because: (1) coupling
strength among midbody ganglia is approximately symmetrical
and (2) the U-shaped period gradient tends to generate
nonuniform intersegmental phase lags. Early modeling studies,
relying on graphical analysis or modeling with electronic
neuromimes suggested that the asymmetry in the identified,
intersegmental interactions could account, at least qualitatively,
for the observed phase lags (Friesen and Stent, 1977; Friesen
et al., 1978; Friesen and Pearce, 1993). A subsequent computer
study incorporated the swim circuits as a chain of phase
oscillators coupled by multiple signal channels to mimic
the intersegmental connections among ganglia. This model
successfully accounted quantitatively for normal intersegmental phase delays, for increases in phase lag when the number of
connected ganglia is reduced, and for alterations in phase lags
induced by lesions and temperature manipulations (Pearce and
Friesen, 1988). Mostly recently, a more biophysical, systems
approach to modeling intersegmental coordination demonstrated again that the observed phase relationships are predicted
307
by the identified intersegmental interactions (Zheng et al.,
2004).
A recent refinement and extension of the coupled phase
oscillator model was used to study further the role of
interactions within the nerve cord and between the central
circuit and sensory feedback in intersegmental coordination
(Cang and Friesen, 2002). This model is more realistic than its
immediate predecessor because channels mimicking intersegmental interactions were limited to those identified experimentally (Fig. 20A). Moreover, the approach was to constrain
the model at each step in its construction by experimental
results followed by tests in which model output was compared
to novel experimental data. As in the earlier phase model, phase
shifts of segmental unit oscillators by intersegmental inputs
were specified by phase response curves (PRCs; Fig. 20B).
These curves were shaped through the use of subsidiary
modeling. Relative PRC amplitudes were set to ensure
symmetric intersegmental coupling strengths. The extended
model also incorporated stretch receptors, constrained by the
phase-shift effect of the VSR described above (Cang and
Friesen, 2000). Simulations with this extended model
reproduced a remarkable replica of swimming activity in
leeches, including: (1) intersegmental phase lags of 8–108/
segment that exhibited a small positive period dependence
(Kristan and Calabrese, 1976; Pearce and Friesen, 1984); (2)
increases in phase lag when the length of the nerve cord is
reduced (Pearce and Friesen, 1985b) and (3) intersegmental
coordination with increased phase lags when the nerve cord is
severed in mid body (Yu et al., 1999). Thus the known topology
of the swim circuit can account for intersegmental coordination
even though not all neurons or interactions in this circuit are
identified.
Students of rhythmic motor systems are deeply interested in
the mechanisms that generate the cycle period (Friesen and
Stent, 1978). The initial mechanism proposed for the leech
swim circuits, based on the four pairs of oscillatory INs then
identified (Friesen et al., 1976, 1978), focused on intra- and
intersegmental interactions that form closed loops of inhibition
(recurrent cyclic inhibition, RCI; Székely, 1964). Such
inhibitory loops can generate stable oscillations with multiple
intrasegmental phases and, concomitantly, intersegmental
phase lags. The validity of this model was supported by
graphical analysis and neuromime simulations, which yielded
cycle periods and both intra- and intersegmental phase
relationships similar to those observed in isolated nerve cord
preparations (Friesen and Stent, 1977). Subsequent experiments, which demonstrated that an isolated ganglion could
generate swim-like oscillations (Weeks, 1981; HashemzadehGargari and Friesen, 1989; Hocker et al., 2000), cast the RCI
model into doubt and supported reciprocal inhibition (RI),
which generates antiphasic oscillations (Brown, 1911; Friesen,
1989d; Brodfuehrer et al., 1995b). The RI model is currently
invoked to account for locomotory oscillations in many species
(Friesen, 1994), including lamprey. Perhaps the question of
whether RCI or RI generates the swimming rhythm in leeches
poses a false dichotomy. Weak, long-period oscillations with
inappropriate phase relationships (Weeks, 1981; Hashemza-
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Fig. 20. Modeling and overview of the leech swim system. (A) Reduced
circuit diagram employed to model intersegmental coordination. Cell numbers
and phase lags as in Fig. 15. (B) Functional model: individual ganglia are
modeled as phase oscillators that interact via signals transmitted during the
active sector of any given neuron (shaded arcs). The signals advance or delay
the phase of the local oscillator in accordance with physiologically realistic
phase response curves. This simple model can account well for coordination in
isolated nerve cords. (C) Schematic model of the complete swim system.
Tension in the body wall, caused both by MN output and the environment,
provides sensory feedback to the central swim oscillator circuits, thereby
modulating both cycle period and phase in the intact leech. Symbols: SR,
stretch receptor; , central oscillator (with intra- and intersegmental components). With the addition of sensory feedback and mechanical coupling
between segments, the phase model generates a realistic simulation of swimming in the intact leech. Parts A and B are modified from Cang and Friesen,
2002; part C is modified from Yu, 2001.
deh-Gargari and Friesen, 1989) might be generated by RI,
whereas the robust multiphasic oscillations recorded from
preparations comprising multiple segments of the nerve cord,
might be the result of known and other, as yet unidentified,
intersegmental inhibitory loops. Given the weakness of
segmental oscillators and the strength and broad extent of
intersegmental interactions, the conclusion that intersegmental
interactions are essential components of the swim oscillator
seems inescapable.
3.4.8. Gaps in our current knowledge
One obvious gap in the description of the leech swim circuit
is that additional oscillatory INs and their interactions await
identification. Two of the identified neurons – cells 60 and 33 –
lack identified inputs. Additionally, intersegmental targets for
cells 115 and 60 remain unknown (Fig. 15). A scanning
technique, in which groups of neurons are depolarized by
focal increases in extracellular K+ concentrations (Friesen
and Brodfuehrer, 1984), cutting-edge optical imaging
(Cacciatore et al., 1999; Taylor et al., 2003; Briggman
et al., 2005), and intracellular injections of dual tracer dyes
(Alexa and Neurobiotin; Fan et al., 2005; Fan and Friesen,
2005) are proving helpful in locating the missing neurons.
Detailed information on the strength of synaptic interactions
and the biophysics of INs is needed, particularly for the
construction of detailed biophysical models. Also, the
physiology and links with the swim oscillator of only one
segmental stretch receptor has been studied thoroughly; many
more mapping studies are need to determine how the DSR and
other stretch receptors provide sensory feedback to control
cycle period and intersegmental phase lags. Moreover, there is
limited knowledge of the biomechanics of DLM activation—
contraction strengths, rates of contraction, and length-tension
relationships (Miller, 1975; Mason and Kristan, 1982; Wilson
et al., 1996a). At the functional level, little is known about the
control of swimming in leeches. For example, how does the
brief output of trigger neurons lead to prolonged excitation in
swim-gating neurons (Brodfuehrer and Thorogood, 2001)?
Finally, the relative importance of central oscillators versus
sensory-central loops in setting cycle period and intersegmental phase relationships in intact leeches or, for that matter,
in other animals (Pearson and Ramirez, 1997) remains largely
unknown. Even prior to more complete information, it is not
premature to develop computer models of leech swimming
like those already available for lamprey (Ekeberg and
Grillner, 1999), which encompass the central neuronal
oscillator, muscle mechanics, and sensory feedback. Such a
complete model would provide a quantitative description of
the leech swimming system presented schematically in
Fig. 20C.
3.5. Vermiform crawling
3.5.1. Behavior
Vermiform crawling is a rhythmic behavior with two major
phases – extension and contraction – that are typically
coordinated with attachment and release of the front and back
suckers (Fig. 21). A single cycle starts with a leech fully
contracted and with both suckers attached. The front sucker
releases and the animal starts to extend by contracting circular
muscles. The extension starts at the front end and moves
progressively more posterior. When the body is fully extended,
the front sucker reattaches and the front end begins to contract.
The contraction wave progresses posteriorly, putting tension on
the posterior end of the body. When the contraction wave
reaches about two-thirds of the way to the tail end, the back
sucker actively releases. When the contraction wave reaches the
posterior end, the rear sucker reattaches, thus completing one
step cycle.
In intact leeches, a step takes 3–10 s (Stern-Tomlinson et al.,
1986), although it can take up to 20 s in dissected leeches
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(Baader and Kristan, 1995; Cacciatore et al., 2000). This type
of crawl step is one of two distinct modes of crawling
displayed by leeches (Stern-Tomlinson et al., 1986). The
other form, called ‘‘inch-worm crawl’’ or ‘‘looping’’ is faster
(1–3 s) and the suckers are brought adjacent to one another at
the end of contraction. Inch-worm crawling is seen only when
a leech is under water (it tends to fall on its side when it
attempts to inch-worm in air), and when it is strongly
stimulated (e.g. when pinched or very hungry). Inch-worming
is more efficient because the leech progresses by nearly a
fully-extended body length with each inch-worm step, and
much less (the body length at full extension minus the length
at full contraction) during a vermiform step. Most studies
have focused on vermiform steps, because it is difficult to
provide a leech with the appropriate conditions for inch-
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worming while recording from its nervous system. (Please
note that all further references to ‘‘crawling’’ in this review
refer to vermiform crawling.)
Elongation of a segment is produced by a contraction of
the circular muscles in a segment, and contraction is produced
by the co-contraction of all the longitudinal muscles in
that segment. For both elongation and contraction, the
behavior begins at the front end and moves smoothly
posteriorly as a wave. Hence, in a single segment, crawling
appears as an alternation of bursting in the circular and
longitudinal MNs. This pattern repeats itself, with a delay, in
successively more posterior segments, thereby producing the
intersegmental progression of the elongation and contraction
waves. This pattern has been recorded in semi-intact (Baader
and Kristan, 1992), and isolated nervous systems (Eisenhart
et al., 2000).
3.5.2. Kinematics
To characterize the movement patterns during crawling,
pieces of white sutures were knotted onto the skin of many
segments and the animals were video-taped as they crawled
(Fig. 22A; Stern-Tomlinson et al., 1986; Cacciatore et al.,
2000). These analyses showed that the elongation phase
was significantly more prolonged in slower crawl cycles,
and that the contraction phase was short and varied
significantly less with step cycle duration (Fig. 22B). In
addition to variations in the elongation phase, step cycles
also got longer because the delay from rear sucker placement
and front sucker release increased with step cycle period.
The average contraction and elongation duration diminishes
as the wave gets to the posterior end of the animal
(Fig. 22C(i)), reflecting the fact that the front of the animal
stays contracted (or elongated) as the wave passes to the
posterior end.
3.5.3. Motor neuron activity
The motor neuronal activity recorded in both semi-intact and
isolated nerve cord preparations is fully consonant with the
kinematic data; circular muscle MNs produce impulse bursts in
each segment that alternate with bursts in MNs that innervate
longitudinal muscle, and the bursts appear progressively later in
more posterior segments in both semi-intact preparations
(Fig. 22C(ii)) and isolated nerve cords (Fig. 22C(iii)). Although
the cycle periods in these two preparations are much longer
than in intact animals, the coordination is the same in all three
preparations; data for intersegmental travel time (ISTT) plotted
against step duration for both dissected preparations fall on the
regression lines calculated from the data for the intact animals
(Fig. 22B; Cacciatore et al., 2000).
Fig. 21. Schematic diagram of crawling behavior. This is a more detailed
drawing of vermiform crawling than was provided in Fig. 3D. This figure
emphasizes two features: (1) both the elongation and the contraction move
slowly from front-to-back along the body and (2) there is a phase in the middle
of the step when the back end of the animal is elongating while the front end is
beginning to contract (from Cacciatore et al., 2000).
3.5.4. Sensory input
Although the basic crawling motor pattern produced in the
isolated nervous system has the essential features seen in more
intact preparations, there are several differences:
A. The period is much longer than in intact animals (as long as
30 s);
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Fig. 22. Kinematics of crawling. (A) Segmental progression of the crawling step. Threads were sewn onto the skin of a leech (image on the left), which was videotaped as it crawled. Distances were measured from the head end to segment 2 (H-2), from segment 18 to the tail end (18-T), and between all other adjacent segments.
Three plots of length vs. time are shown for the front end (H-2), back end (18-T), and one segment in the middle (10). Both the elongations and the contractions move
from front-to-back, with contractions appearing to move through the animal faster than elongations. (B) Plot of the rate at which elongations and contractions move
through the animal, the intersegmental travel time (ISTT) as a function of the duration of step cycles. The lines are the linear regressions for the elongation (open
circles) and contractions (closed circles). The slopes of the two lines are significantly different, showing that elongations move through the leech’s body more slowly
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311
B. There is much more variability in the period than in either
fully intact or semi-intact preparations and
C. The duration of the bursts is greater than in semi-intact
preparations. The bursts are so long that the circular motor
bursts (elongation) overlap with the longitudinal motor
bursts (contraction), particularly in the rear end of the nerve
cord because the contractions occur relatively closer to the
elongations in the rear of the animal (Fig. 22C).
These differences are likely due to the lack of sensory input
in the isolated nerve cord, which in the intact animal supplies
tonic excitation as well as cycle-by-cycle feedback, and
regularizes and shortens the motor bursts. The source of the
sensory feedback is not known.
In addition to the effects of sensory input on the basic motor
pattern, other, mostly anecdotal observations suggest that
crawling is a highly variable behavior. For instance, mechanosensory stimuli delivered to the rear of the animal during
extension will often cause it to release its rear sucker and swim
away. In fact, leeches sometimes make this transition from
elongation to swimming spontaneously, with no obvious
stimulation of any sort. Identical stimuli delivered to a
contracting leech will speed up the contraction, but never
evoke swimming. (In fact, it seems impossible to elicit
swimming in a leech with its front sucker attached.)
3.5.5. Models
Two kinds of models have been proposed to explain leech
crawling. The first is a neuronal model that simulates the MN
patterns along the nerve cord, and the second is a
biomechanical model that explains how the motor patterns
produce movements.
In the neuronal model (Fig. 23), there is a single oscillator at
the anterior end of the nerve cord that drives a motor controller
for elongation, and a second one for contraction in the first body
segment. Each segment has its own set of elongation and
contraction motor controllers that are driven by the homologous
controller in the previous segment. To produce a realistic motor
output, the motor controllers must have positive feedback
within a segment; to produce the full range of cycle periods
observed, there must be parallel input to every segment that
provides the same level of tonic input to all segments. The
magnitude of this tonic input controls the cycle period (the
greater the tonic input, the shorter the cycle period), although
positive feedback is required for the tonic input to exert its
effect. Without positive feedback, increasing the tonic input has
very little effect on the period. This model is constrained by a
large set of kinematic data and recordings from MNs in semiintact (Baader and Kristan, 1995) and isolated nerve cords
(Eisenhart et al., 2000), including the effects of a variety of
Fig. 23. Neuronal model of crawling. (A) The simplest model that produced a
crawling step. The connections within each segment produce an elongation
followed by a contraction (represented as an E/C unit) when the front segment is
provided with tonic excitatory input (is). Positive feedback is needed in each E/
C unit to match the observation that the MNs fire at a frequency independent of
the step duration. Shown to the right are the firings of just the circular MNs
(elongation units) in 10 of the segments from front-to-back. Note that increasing
the strength of is (the serial input) increases the MN firing rate slightly, but did
not change the rate of progression along the cord; i.e. the ISTT is relatively
constant. (B) The same circuit, with the addition of a parallel input, Ip, to each
segmental E/C unit. With varying strength of Ip, the ISTT varies over a range
comparable to that seen in the intact animal (figures from Cacciatore et al.,
2000).
lesions of nerves and connectives (Stern-Tomlinson et al., 1986;
Baader and Kristan, 1995; Cacciatore et al., 2000), but it awaits
identification of any of the elements of the pattern generators.
A biomechanical model of leech movements has been
constructed (Skierczynski et al., 1996) based upon the anatomy
of the leech, passive mechanical properties of the body wall
(Wilson et al., 1996a), contractile properties of the muscles
(Mason and Kristan, 1982; Wilson et al., 1996a), and pressures
measured in the body lumen as leeches crawl, swim, and
shorten (Wilson et al., 1996b). The model incorporates three
muscle layers: longitudinal, circular, and dorsoventral (flattener). These features are shown in Fig. 24A. The model
assumes that each segment maintains a constant volume at all
times, and that MNs cause muscle shortening in only their own
segment. Because of this geometry, longitudinal muscles
(which shorten a segment) are antagonists to both the circular
and the flattener muscles (both of which elongate a segment by
decreasing its cross-sectional area, although they produce very
different shapes). To produce movements, bursts of MN spikes
cause muscle tension and shortening. Both are important: a low
than contractions. (C) Summary of the MN activity patterns measured from intact animals (i), semi-intact preparations (ii), and isolated nerve cord preparations (iii).
Open boxes show the average time spent in contraction and shaded boxes show the average time spent in contraction. Data for the intact animals (i) were estimated
from the durations of the contractions and relaxations of the individual segments, from data like those in A. For semi-intact (ii) and isolated nerve cord (iii)
preparations, durations of bursts in longitudinal (contraction) and circular (elongation) MNs were collected. Two features stand out: (1) the more intact the
preparation, the shorter the step cycle duration, and (2) contractions and elongations were distinct in intact and semi-intact animals, whereas they overlapped
significantly in isolated nerve cord preparations (from Cacciatore et al., 2000).
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Fig. 24. Biomechanical model of crawling. (A) Features used in the model of a
single segment. The cross-section of the leech is ovoidal, with each of the four
semi-axes of the ellipse (a1, a2, b1, b2) independently variable, determined by
the circular muscle activity in each of the four quadrants. The length (L) of each
segment is controlled directly by the activity of four longitudinal muscle bands
attached to the top, bottom, and sides of the ellipses. (B) Extension phases of
the crawling movements predicted by a 16-segment model of the leech. The
data used to drive the segments are those measured from kinematics (i) and
from motor neuronal bursts in semi-intact (ii) and isolated nerve cord preparations (iii), as shown in Fig. 22C. Three frames are shown for each
condition, representing the beginning of the extension (top image), the midst
of the elongation (middle image), and the end of the elongation (bottom
image). The times to achieve these states varied in the three cases (from Kristan
et al., 2000).
energy was calculated (Skierczynski et al., 1996). Sixteen
segments were modeled (14 midbody segments were identical;
the most anterior three segments were lumped with the head
into one larger segment, and the most posterior two segments
were lumped with the tail); the shape of the model
approximates a leech fairly well (Skierczynski et al., 1996;
Kristan et al., 2000). When the motor bursts estimated from the
kinematics were used, the movements looked very much like a
crawling leech (Fig. 24B). This is not surprising because the
model was tuned to produce these movements. What was
surprising was that although the pressures generated by the
model were in no way constrained, they produced amplitudes
that were very close to those measured in a crawling leech. In
addition, the motor bursts recorded in semi-intact animals
produced a slower and less efficient crawl, but the modeled
animal appeared to be crawling. When motor bursts from the
isolated nerve cord were used to drive the model, however, the
behavior was very wrong in at least two ways: (1) the internal
pressures in the model were much larger than any ever recorded
from a real leech; (2) parts of the animal were contracting while
other parts were elongating, so that the crawl steps were
extremely inefficient—very little progress was made with each
step. These results suggest why MN burst patterns during
crawling closely match the biomechanics in which they
operate. For instance, the large pressures seen in (1) occur
because circular MNs overlap somewhat with longitudinal
bursts, a condition never seen in an intact or semi-intact animal.
This suggests that sensory feedback can be used to tune a CPG
to work in a biomechanically efficient range of its capability.
Other biomechanical models have been used to simulate
leech crawling (Wadepuhl and Beyn, 1989; Alscher and Beyn,
1998) and swimming (Jordan, 1998). These models use very
simplified geometries, but more sophisticated mathematics for
the crawling model (Alscher and Beyn, 1998), and a realistic
model of animal/substrate coupling in the swimming model
(Jordan, 1998). All of these models will be aided by more
information about the neuronal circuits underlying the
behaviors.
level of sustained tension in antagonistic muscles is required to
produce a rigidity that serves as a hydroskeleton for muscles to
produce movements. (Contraction of one muscle in an
otherwise flaccid segment produces very little movement of
anything but itself; if the segment is rigid, a single muscle can
move the whole segment).
The biomechanical model was applied first to crawling.
Motor neuronal bursts from isolated nerve cords (Eisenhart
et al., 2000) and from semi-intact preparations (Baader and
Kristan, 1992) were used to make estimates of the motor bursts
from the kinematic studies. In each time bin (corresponding to
0.6 s), the firing of each MN excited its appropriate muscle,
which generated tension until that segment achieved its steadystate shape (Mason and Kristan, 1982). The only mechanical
interaction among segments was imposed by the constraint that
adjacent segments share a cross-sectional face. To find a steadystate shape for the whole animal, the minimum total potential
3.5.6. Initiation of crawling
Intracellular activation of several identified neurons in the
anterior brain of a leech can elicit overt behavior in semi-intact
and fictive behavior in isolated nerve cord preparations.
Examples include swim initiation by trigger neurons and SE
cells. Another such neuron, cell R3b1, which is located in the
third neuromere of the subesophageal ganglion, can initiate the
crawling motor pattern in both semi-intact and isolated nerve
cord preparations (Esch et al., 2002). It is effectively a ‘‘crawl
gating’’ neuron because crawling stops when the depolarization
is released. During a crawl episode initiated in another way (e.g.
by stimulation of the posterior end), R3b1 oscillates above its
resting potential. Surprisingly, depolarizing R3b1 can also
produce swimming, or a combination of swimming and
crawling. During the latter, hybrid behavior, swimming bursts
occur during the extension phase of crawling; this odd behavior
is sometimes seen even in intact animals when they are in very
shallow water.
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3.5.7. Gaps in our current knowledge
Crawling behavior is well characterized kinematically, but
little is known about the neuronal basis of its CPG. Because the
crawling pattern can be elicited in isolated nerve cords
(Eisenhart et al., 2000; Briggman et al., 2005), identifying the
CPG should be a relatively straight-forward task. The neuronal
model (Cacciatore et al., 2000) suggests a simple explanation
for intersegmental coordination, but is agnostic about the
location and nature of the underlying neuronal circuits. An
appealing feature of the isolated nerve cord preparation for
studying crawling is the possibility of studying the contextdependence of behavioral choice. For example, in both intact
animals and isolated nerve cords, appropriate stimulation
reliably triggers swimming, whereas the same stimuli delivered
during the contraction phase of crawling speeds up the
contraction but never leads to swimming (Eisenhart et al.,
2000).
3.6. Feeding
All leech species feed by ingesting prey or blood through
their anterior sucker (Sawyer, 1986). The anterior sucker is
attached to the host or prey, and a muscular pharynx just
posterior to the esophagus produces suction by contraction of
extrinsic muscles that pull open the lumen of the pharynx. A
major distinction among leech families is whether they ingest
their prey whole or suck its blood or other internal fluids. The
medicinal leech attaches its front only to hosts much larger than
itself, rasps through the host’s skin with eversible jaws, and
sucks the blood that oozes from the tripartite wound.
Anticoagulants and anti-platelet substances are exuded into
the wound to keep blood oozing from the cut during the 10–
30 min that it takes a leech to complete feeding. In fact, blood
continues to ooze from the leech bite for several hours after the
leech completes its meal. This ability to remove quantities of
blood was the initial appeal of leeches medicinally (Payton,
1981). They are used in modern medicine to keep blood flowing
through surgically replaced appendages until their venous
supply can be re-established, usually after 3–4 days (Whitaker
et al., 2004).
3.6.1. Behavior
While medicinal leeches feed, they produce slow peristaltic
movements (Fig. 25), both front-to-back and back-to-front
(Wilson and Kleinhaus, 2000). These movements serve to
propel the blood rearward into the gut and lateral gut pouches
present in segments 7–13. Blood can be stored in these pouches
for several months without decaying. Ultimately, the blood is
moved into the intestine, where commensal bacteria help to
digest the red blood cells (Braschler et al., 2003). While they
are feeding, leeches are unresponsive even to very strong
mechanosensory stimulation that would normally elicit a
vigorous response (Misell et al., 1998). In fact, leeches can be
cut open while they are feeding and will continue to eat (Lent,
1985). Such half leeches, or semi-intact leeches with only the
head and a few anterior segments intact (Wilson et al., 1996;
Zhang et al., 2000), will feed for longer times than normal,
Fig. 25. Feeding behavior. (A) Measuring muscle activity in seven locations
along the leech as it feeds. The drawing shows a leech attached to a membrane
stretched over a tube containing blood. Bipolar EMG electrodes have been
inserted into the longitudinal muscles at seven locations along the leech’s body,
in segments 9–15. (B) EMG recordings during peristaltic movements from front
to back. The large gray arrows indicate the direction of the peristalsis. (C)
Recordings in the same animal when the peristalsis initially progressed back-tofront, then reversed about midway through the recording to go front-to-back for
two cycles, only to reverse again near the end of the recording (from Wilson and
Kleinhaus, 2000).
presumably because gut filling is part of the feedback
mechanism to terminate swimming (Lent and Dickinson,
1987).
For many weeks after feeding, leeches are sluggish and
passive. They tend to hide and, when disturbed, withdraw rather
than advance or locomote (Lent and Dickinson, 1984; Groome
et al., 1993). Hungry leeches, on the other hand, are found near
the surface of a pond, are very active, and tend to move toward a
disturbance rather than to withdraw from it (Sawyer, 1981,
1986; Lent et al., 1988). In the lab, hungry leeches orient toward
the source of surface water waves and then swim towards that
source (Young et al., 1981). This behavior can be elicited by the
water waves in the dark and or visually by shadow waves
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without any actual water movements (Carlton and McVean,
1993). Hence, leeches use both mechanosensory and visual
signals to orient toward the source of waves in the water.
Leeches bite surfaces that are warm and/or have appropriate
chemical stimuli. For instance, they will bite through warmed
parafilm (Dickinson and Lent, 1984), and they will bite through
a room-temperature sausage casing filled with blood or a blood
surrogate (Galun and Kindler, 1966). Thermal receptors have
not yet been identified in leeches, but a fair amount is known
about the chemoreceptors.
3.6.2. Chemosensation
Galun and collaborators were the first to study chemosensation, by testing the attraction of leeches to various organic
substances (Galun and Kindler, 1966). They found that leeches
would feed on several sugars and amino acids, particularly
glucose, galactose and arginine, but only if the solution also
contained Na+ (Galun, 1975; Elliott, 1986). The site of these
receptors was localized to sensory placodes on the upper lip of
the anterior sucker (Elliott, 1987). Receptors send their axons
along particular nerves into the anterior brain (Perruccio and
Kleinhaus, 1996). These receptors respond to the same
substances that leeches avidly feed upon (Li et al., 2001).
Interestingly, both the basal level of chemosensory activity and
their evoked responses are decreased by adding quinidine or
denatonium to the mix, substances that inhibit feeding
responses (Kornreich and Kleinhaus, 1999).
3.6.3. Motor patterns
As leeches prepare to feed, they attach their anterior suckers
to the prey and elongate and stiffen the head region (Fig. 25A;
Wilson and Kleinhaus, 2000). The rasping jaws are then everted
through the open mouth with the rasping teeth pressed against
the substrate to be cut (usually the skin of the host animal), and
the teeth are pulled back and forth (Dickinson and Lent, 1984).
As fluid flows through the cut, the pharynx starts to contract
rhythmically to extract the fluid and move it into the gut. As
fluid collects in the gut, it is moved around by peristaltic
movements of the body (Fig. 25).
Nothing is known about the control of head positioning or
the rasping movements, however, dopamine may play a role
because the accessory ganglia and musculature associated with
the three jaws is richly innervated by dopaminergic fibers
(Crisp et al., 2002). Movements can be elicited in the isolated
pharynx by a variety of transmitters and modulatory substances,
including 5-HT (Lent et al., 1989; O’Gara et al., 1999b), ACh
(O’Gara et al., 1999a), and FMRFamide (Li and Calabrese,
1987; O’Gara et al., 2000). Hence, it is possible that there is no
neuronal pattern generator for pharyngeal pumping; instead,
this component of feeding might be activated by neuromodulators released onto the pharyngeal muscles directly. The
peristaltic rhythm that moves blood around inside the gut is
expressed by muscles in the body wall that can be recorded with
EMG electrodes placed into the body wall of a feeding leech
(Wilson and Kleinhaus, 2000). (The esophagus is cannulated to
collect the blood or other fluid that is being pumped into the
alimentary tract.) The peristaltic waves can originate in a
variety of segments, and the waves can move either forward or
backward. The nature of the pattern generator is not known.
Saliva is released from the giant (greater that 200 mm in
diameter) salivary gland cells. These cells produce salivary
secretions into a tubular system that opens into the esophagus
near the mouth opening (Lent et al., 1989). These glandular
cells produce very broad, Ca2+-dependent action potentials
(Marshall and Lent, 1984). These cells are activated by adding
5-HT to the bathing solution or by stimulating serotonergic
neurons (Marshall and Lent, 1988). Hence, it is possible that the
release of saliva – with its anticoagulants, anti-platelet
substances, and local anesthetic – is produced by 5-HT
released into the leech’s bloodstream by the same sensory cues
that trigger the start of biting.
The duration of a feeding episode is affected by a variety of
influences. Inflating the body with blood, saline, or even air can
terminate an episode prematurely, and removing the ingested
blood can greatly prolong an episode (Lent and Dickinson,
1987). This suggests that stretch receptors (in the alimentary
tract or even in the body wall) help to stop feeding. There is no
evidence that the chemical nature of the ingested liquid
influences the duration of feeding: leeches ingest blood and
arginine/NaCl solutions indistinguishably. However, there does
appear to be a chemoreceptor in the gut responsive to noxious
stimuli because leeches quickly terminated a feeding episode
when quinidine was added to the ingested solution (Kornreich
and Kleinhaus, 1999).
3.6.4. Regulation and plasticity
The close association of 5-HT with feeding has suggested
that 5-HT, acting either as a hormone or as a neurotransmitter,
may be the signal that regulates feeding: it builds up as the
animal gets hungrier and triggers both food-seeking behaviors
and the onset of ingestion (Lent, 1985; Lent and Dickinson,
1984, 1988). The localization of 5-HT in the nervous system
and elsewhere presents a complex picture (Lent et al., 1991;
Groome et al., 1993), and the relationship of serotonergic
neurons to feeding is complicated at best (Groome et al., 1995;
Goldburt et al., 1994; Wilson et al., 1996). A carnivorous leech,
Haemopis marmarota, has been classically conditioned to
avoid one of two kinds of meat (chicken versus liver) by pairing
either of them with quinine and an aversive stimulus (Karrer
and Sahley, 1988). Because of the long times between meals in
sanguivorous leeches, similar experiments have not been
attempted in Hirudo.
3.6.5. Gaps in our knowledge
As for crawling behavior, very little is known about the
CPGs for either food ingestion or peristalsis. To date, feeding
activity has not been described in an isolated nerve cord
preparation, making identification of the relevant neuronal
circuits nearly impossible. It might, however, be possible to
locate the peristalsis CPG in a semi-intact preparation
consisting of an intact front third of the animal, with much
of the rear two-thirds dissected away to expose the nerve cord
(Zhang et al., 2000). Because neuronal responses to food can be
detected in preparations of the dorsal lip attached to an
W.B. Kristan Jr. et al. / Progress in Neurobiology 76 (2005) 279–327
315
otherwise isolated nerve cord (P.D. Brodfuehrer, personal
communication), it may even be feasible to identify INs related
to feeding behavior in the brain.
3.7. Interactions among behaviors
Local bending occurs during crawling (Cacciatore et al.,
2000). This bending is probably used to avoid rubbing the skin
on foliage and rocks as the animal crawls along the bottom of a
pond. Local bending does not appear to occur during swimming
(Kristan and Stent, 1975). In part, this may be because the body
wall is held too rigid during swimming to see indentations
caused by light touch, or because some of the LBIs are also
CPG neurons for swimming (Lockery and Kristan, 1990b).
Local bending also cannot be elicited during feeding, nor can
any other behavior (Misell et al., 1998). It appears that the act of
feeding turns off all other behaviors, including any that can be
elicited by even very strong somatosensory stimulation.
Shortening always predominates over swimming, whether
the animal is at rest (and stimuli that would produce shortening
and swimming are presented simultaneously) or is in the midst
of swimming (Shaw and Kristan, 1995). In part, this inhibition
of swimming results from a strong inhibition of cell 204, one of
the most effective swim gating INs (Shaw and Kristan, 1997). A
surprising result of these studies was that other swim gating
INs, as well as both types of swim trigger INs tested, were
actually excited by stimuli that elicited shortening (Fig. 26).
This means that several INs that elicit swimming when
stimulated individually are excited when shortening is elicited.
Minimally, this implies that decision-making INs are not
dedicated to particular behaviors, but are instead multifunctional; i.e. they are active during two or more different
behaviors. This result has spawned the hypothesis that such coactivation is a necessary part of decision-making, so that all
behavioral decisions are made by a combinatorial code of such
multiplexed INs (Fig. 27).
In line with this interpretation, cell R3b1, produces either
swimming or crawling in a context-dependent manner, and is
inhibited when shortening is elicited (Fig. 28; Esch et al., 2002;
Esch and Kristan, 2002). When R3b1 is stimulated in semiintact leeches, the expression of behavior depends on the depth
of the water surrounding the intact part of the leech: in deep
water, it swims, whereas in shallow water or on dry land, it
crawls (Fig. 28). Thus another effect of sensory feedback on
crawling is the determination of behavioral choice. Cell R3b1
appears to narrow the behavioral choice to two options –
swimming or crawling – and the final choice is made on
the bases of sensory feedback related to the water level.
Interneurons in the posterior brain also are probably important
for crawling behavior, because some neurons in this ganglion
have activity that is phase-locked to the crawl cycle (Baader and
Bachtold, 1997).
In isolated nerve cords, electrically stimulating nerves at
different segmental locations elicits different motor patterns:
stimulating nerves near the anterior end reliably elicits
shortening (Shaw and Kristan, 1997), stimulating at the
posterior end elicits crawling (Eisenhart et al., 2000), and
Fig. 26. Interactions between the circuits underlying shortening and swimming
behaviors. Turning on shortening, by activating T and P mechanoreceptors near
the front of the animal turns off swimming. The connections from the pathway
that triggers shortening are shown. Surprisingly, most of the connections are
excitatory. The letters and numbers inside circles represent the identities of the
neurons (e.g. cells 204, 61, and 21 are individual segmental neurons) or cell
types: oscillator neurons are of three varieties – 40, 150 and 2408 – based upon
the phase of their oscillation, and the MNs are of four sorts (DE, VE, DI, and VI)
as explained previously. The shaded boxes enclose neurons with similar
function.
stimulating somewhat away from the posterior end produces
swimming (Kristan and Calabrese, 1976). Stimulating intermediate segments sometimes elicits swimming and other times
crawling. Recording the activity of many neurons (up to 150)
with voltage-sensitive dyes revealed a small number of neurons
whose membrane potential trajectories predicted which
behavior would be selected (Briggman et al., 2005).
Depolarizing and hyperpolarizing one neuron, cell 208, biased
the choice toward either swimming or crawling. Interestingly,
cell 208 had previously been shown to be a member of the CPG
Fig. 27. Summary of the proposed combinatorial code for making behavioral
decisions. This schematic diagram indicated which neurons are activated (black
rectangle) during four different behaviors, based upon intracellular recordings
from these neurons as each of the behaviors is activated. Three cell types have
been documented; for these the cell numbers of representative examples are
indicated below the cell types. Two others (D and E) represent other types of
neurons, as yet unidentified, that could help to explain how behaviors are
chosen.
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W.B. Kristan Jr. et al. / Progress in Neurobiology 76 (2005) 279–327
through the release of FMRFamide; Kuhlman et al., 1985a,b)
implying that modulation of the heartbeat circuitry occurs in
parallel with activation of the swim CPG. Other behaviors such
as shortening and exploratory movements similarly show
parallel modulation of the heartbeat circuitry, albeit it these
effects have not be tested in the absence of sensory feedback as
with swimming.
3.8. Methodologies and approaches for further research
Several lines of research and experimental techniques have
been applied to leech nervous systems, but have not yet
culminated in deep insights into behavioral circuits, their
origins during development and functional plasticity. This
section briefly presents several of these, pointing out their
future potential for contributing to our understanding of how
leech behaviors are produced.
Fig. 28. Stimulating a single IN (R3b1) produces different behaviors, depending upon the sensory context. (A) Semi-intact preparation used to stimulate
R3b1 in the subesophageal ganglion of the anterior brain. The brain and first
three segmental ganglia are denervated and pinned out; the rest of the animal is
free to move. Cell R3b1 was stimulated with identical stimuli three times in one
preparation, evoking three different behaviors (B–D). (B) When the water level
was low (less than the thickness of the animal’s body at rest), the animal always
crawled. E and C represent periods of elongation and contraction determined by
observing the movements in the intact part of the animal. The MN in the DP
nerve recording in segment 3 bursts in phase with the behavior. (C) When the
water level was high (greater than four times the animal’s thickness at rest), the
animal swam, as determined both by observing its movements and from the
1 Hz oscillations of the motor bursts in the nerve recording (see expanded
trace). (D) At an intermediate water depth (slightly deeper than the thickness of
the animal), the leech would sometimes produce a combined crawl and swim:
it would go through elongations and contractions typical of crawling, with
episodes of swimming during the elongation phase of the crawl (from
Esch et al., 2002).
for swimming, although an unusual one—it is the only unpaired
CPG neuron, and the only one that makes excitatory
connections to other CPG neurons (Weeks, 1982a,b,c).
Taking a more global approach, the patterns of spontaneous
movements in intact leeches were analyzed, using an automated
tracking system, for patterns of such distinguishable movements as swimming, crawling, ventilating, and searching over
the course of days (Mazzoni et al., 2005). There appeared to be
some distinct patterns in the sequences of movements,
suggesting that internal states of the animal produce choices
of behaviors in a somewhat ordered (non-random) way (GarciaPerez et al., 2005).
The rate of the heartbeat increases during swimming (Arbas
and Calabrese, 1984). This increase can be triggered in the
isolated nervous system by depolarization of cell 204 (perhaps
3.8.1. Functional indicator dyes
The leech nervous system has proven useful for developing
dyes sensitive to the electrical potential across the neuronal
membrane (Salzberg et al., 1973; Canepari et al., 1996) and
for tracking down neuronal connections (Farber and Grinvald,
1983). A class of fluorescent dyes with significantly higher
sensitivity was developed recently (Gonzalez and Tsien,
1997). These made possible the recording of many neurons at
once during motor pattern generation in a segmental ganglion
(Cacciatore et al., 1999). Fluorescence changes are produced
by fluorescent resonant energy transfer (FRET) between two
dyes that are dissolved into the membranes of neuronal
somata. One of the dyes remains at the outer face of the
membrane and is immobile; the other dye dissolves in the
lipid bilayer of the membrane, is mobile, and carries a small
negative charge (Fig. 29A). The mobile molecule moves near
the inside surface when the membrane is depolarized and near
the outer surface when the cell is hyperpolarized (Fig. 29A
and B). To generate a signal that indicates the membrane
potential, the cells are illuminated with a wavelength
absorbed by the immobile molecule. When the mobile
molecule is far from the stationary one (i.e. the cell is
depolarized), the stationary molecule emits a photon at its
characteristic wavelength. When the two types of dye
molecules are close together (i.e. the cell is hyperpolarized),
the mobile molecule absorbs some of the energy from
the immobile molecule and emits a photon at a longer
wavelength. A useful dye combination is coumarin and
oxonol, when these are illuminated with violet light. If it is
hyperpolarized, the neuron membrane emits mostly blue
light; when the neuron depolarizes, the blue signal decreases
and the red signal increases. This dye combination has a
significantly greater signal-to-noise ratio than earlier voltagesensitive dyes. With improved CCD cameras, voltage changes
of 2 mV can be distinguished in small neurons (Taylor et al.,
2003). As an example, in recording from a ganglion in an
isolated nerve cord (Fig. 29C), the intensity of fluorescence
in MNs varies significantly during swimming activity
(Fig. 29D).
W.B. Kristan Jr. et al. / Progress in Neurobiology 76 (2005) 279–327
317
Fig. 29. Using voltage-sensitive dyes to measure activity of neurons during the production of behaviors. (A and B) Schematic diagram of the voltage-sensitive dyes, based
upon fluorescence resonance energy transfer (FRET). Two fluorescent molecules are dissolved into the membrane. One (light gray) remains at the outer surface of the
membrane, whereas the other (dark gray), which is dissolved within the membrane, moves to whichever surface is more positively charged, owing to its own small negative
charge. Light of an appropriate wavelength is shown on the membrane to excite the molecule immobilized at the surface of the membrane. If the membrane is depolarized
(A), the excited molecules emit photons of their characteristic fluorescence. If, instead, the membrane is hyperpolarized (B), the mobile molecule collects near the outer
surface of the membrane, very close to the immobilized molecule. In this case, the mobile molecule absorbs some of the energy from the immobile molecule and emits
photons at its own, longer wavelength. Therefore, the intensity of the photon emissions from either molecule – or better, the ratio of the emissions from the two – provides a
measure of changes in the membrane potential. (C) Measuring the intensity of a FRET dye in MNs during the swimming motor program. The diagram shows the
preparation used: a nerve cord from segment 2 through the posterior brain. Ganglion 10 (represented inside the large rectangle) was stained with both dyes and a part of its
dorsal surface (inside the smaller rectangle) was imaged. In addition, extracellular records were obtained from a DP nerve. (D) The DP recording (top recording trace)
shows the MN bursts characteristic of swimming. Above the recording are a series of images of the cluster of neurons at different phases of the swim cycle: the first, third,
and fifth images are during dorsal motor neuronal bursts in the DP nerve recording and the second, fourth, and sixth images are from the interburst periods. Two neuronal
somata are indicated, cell 3 (a dorsal longitudinal MN that fires in phase with the bursts in the DP nerve) and cell 4 (a ventral MN that fires out of phase with the DP bursts).
The bottom two recordings are the averaged intensities of the pixels representing cells 3 and 4, obtained at a rate of 8 Hz. Even at this low rate of data acquisition, the
oscillations in the fluorescence signal – indicating membrane potential oscillations – can be seen without averaging.
As a test of their function, these dyes have been used to
locate oscillator INs that help to generate the swimming motor
pattern (Cacciatore et al., 1999). More recently, they have been
used to locate postsynaptic targets of Tr2 (Taylor et al., 2003),
as well as to record the activity of up to 100 neurons on the
ventral surface of a segmental ganglion in response to stimuli
that sometimes produce swimming and other times nonswimming responses (Briggman et al., 2005). The optical
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analysis required to characterize these responses takes only 10–
15 min, so that neurons whose activity looks interesting can be
recorded intracellularly and their electrophysiological properties tested more directly. They can also be stimulated
individually to determine their effects on the initiation of
behaviors (Taylor et al., 2003). These techniques will be very
useful in tracking down potential decision-making neurons and
the connection among them.
A second imaging technique employs calcium-sensitive
dyes (Ross et al., 1987), which were injected into single leech
neurons, to determine the nature (i.e. spiking versus nonspiking mechanisms) of transmitter release in the heartbeat
CPG (Ivanov and Calabrese, 2000, 2003). These dyes can
potentially be used for making finer distinctions among
neuronal interactions. For instance, Ca2+-sensitive dyes might
be used to record from different branches of the same neuron, in
order to determine whether different inputs affect particular
regions of the dendritic tree differentially. Such localized
synaptic effects could allow different regions of the dendritic
tree to act somewhat autonomously.
3.8.2. Modeling
Several models have been produced to simulate various
aspects of heartbeat (Nadim et al., 1995; Olsen et al., 1995; Hill
et al., 2001, 2002; Jezzini et al., 2004), local bending (Lockery
et al., 1989; Lockery and Sejnowski, 1992, 1993; Lewis and
Kristan, 1998b), swimming (Friesen and Stent, 1977; Pearce
and Friesen, 1988; Taylor et al., 2000; Wolpert et al., 2000;
Wolpert and Friesen, 2000; Friesen and Cang, 2001; Zheng
et al., 2004), and crawling (Cacciatore et al., 2000;
Skierczynski et al., 1996; Kristan et al., 2000). These models
incorporated simplified neuronal properties and a limited
number of simulated neurons. In some cases, the simplifications
were used because the relevant properties were not known. In
other cases, however, the simplifications were required because
of limited computer time and memory. This limitation no
longer exists, so that large and complex models are now readily
accomplished. It should now be possible to make a very
realistic model of intersegmental coordination in the swimming
circuit, for instance, and to determine the nature of the
coordination of the heart MNs as a function of the strengths of
their connections from the heart INs.
3.8.3. Plasticity
Many leech behaviors are plastic, showing both noncontingent changes (e.g. habituation and sensitization) and
contingent changes (e.g. classical conditioning). For example,
the local bend response shows non-contingent learning:
stimulating either the skin or an identified IN produces
sensitization (increased response to a test tactile stimulus) with
at least two different time constants, and stimulating one
particular IN can decrease the size of the response (Lockery and
Kristan, 1991). Because these changes are apparent in MN
responses, the sites of such plasticity surely reside in the central
nervous system, however, the critical synapses that are modified
have not been identified. Moreover, stimulating one P cell at a
high rate also produces sensitization of the local bending
response (Lockery and Kristan, 1991), but it is not known
whether the changes are due to changes in neuronal properties
or to changes in the strengths of synapses. If synaptic strengths
change, it makes a qualitative difference whether the changes
are at the P cell-to-interneuronal synapses or at the IN-to-MN
synapses (see Fig. 9). If the latter synapses change their
strengths, P cell interactions adjacent to the tetanized one would
also be strengthened; i.e. there would be generalization of the
response between sensory pathways.
Whole-body shortening has been used extensively to study
the neuronal bases of behavioral plasticity (Sahley, 1995)—
mostly habituation and sensitization, although this response
also exhibits classical conditioning (Henderson and Strong,
1972; Sahley and Ready, 1988). The starting points for many of
the studies attempting to find a neuronal mechanism of these
behavioral plasticities are the observations that 5-HT induces
changes in whole-body shortening resembling habituation
(Biondi et al., 1982; Belardetti et al., 1982) and dishabituation
(Beron et al., 1987) and that depleting the nervous system of 5HT severely modifies such plasticity (Ehrlich et al., 1992;
Sahley et al., 1994; Modney et al., 1997). A fair amount is
known about the effects of 5-HT on sensitization and
dishabituation (Sahley, 1995), as well as some of the second
messenger pathways involved in these plasticities (Burrell and
Sahley, 2001). In addition, blocking some 5-HT receptors
eliminates sensitization (Beron et al., 1987). The site of 5-HT
action is thought to be the synapse from the sensory neurons to
the INs responsible for shortening and, perhaps, the pathway
from the mechanoreceptors to the Retzius cells (Sahley, 1995),
which are reservoirs for much of the 5-HT contained within
each ganglion (Glover and Lent, 1991). The interconnected
network of S cells carries the sensitizing signal throughout the
body; cutting Faivre’s nerve (Sahley et al., 1994) or the axon of
a single S cell (Modney et al., 1997) prevents sensitization from
spreading beyond the site of the lesion. In an elegant
confirmation that the S cell is necessary for sensitization to
occur, sensitization reappeared after regeneration of the
connection between adjacent S cells that had been previously
ablated (Modney et al., 1997; Burrell et al., 2003).
Bath-applied 5-HT makes the S cell itself more excitable
(Burrell et al., 2002), suggesting one potential site for the
sensitization caused by endogenous release of 5-HT. An
intriguing suggestion is that part of the increased excitability
could be caused by reflections of action potentials in the T and P
mechanosensory neurons, thereby increasing the effectiveness
of the sensory input (Baccus et al., 2000, 2001). Strong stimuli
that induce sensitization strongly activate Retzius cells (Sahley,
1994; Lockery and Kristan, 1991), and 5-HT is released from
the somata of Retzius cells in a paracrine manner (Trueta et al.,
2003, 2004; De-Miguel and Trueta, 2005), thereby delivering
large quantities of 5-HT in much the same way as bath
application. These studies make a very good case that both the S
cell and the Retzius cells are critical for plasticity in wholebody shortening, and that 5-HT is a critical neuromodulator in
the leech.
Tactile stimulation of the leech body wall sufficient to
activate T and P mechanosensory neurons (Nicholls and Baylor,
W.B. Kristan Jr. et al. / Progress in Neurobiology 76 (2005) 279–327
319
1968), often elicits swimming in intact and semi-intact leeches
(Fig. 16). Given this result, it is surprising that stimulation of
individual T and P cells often fails to elicit swimming in
isolated nerve cords. One explanation for this failure is that
responses to stimulation of mechanoreceptors, in both intact
(by mechanical stroking) and isolated preparations (by current
injection), decrements rapidly (Debski and Friesen, 1985,
1987). Successive stimulus trials lead to briefer swim episodes
until swim initiation abruptly ceases. Because leech dissection
involves cutting T, P and N cell axons, strong injury-induced
stimulation of these cells may have induced habituation.
Habituation is most obvious for light touch, which selectively
activates T cells, but also occurs during P and N cell stimulation
(Debski and Friesen, 1987). The degree of habituation is
correlated with a reduction of excitation in swim-gating
neurons, but additional, unknown factors involving these cells
and the central oscillator are also important (Debski and
Friesen, 1986). Bath application of 5-HT does not reverse
habituation of the swimming response to tactile stimulation
(Debski and Friesen, 1987).
3.8.4. Development
The nervous system of the leech, like the rest of its body,
develops in a very stereotyped way (Stent et al., 1992).
Essentially all neurons in a segmental ganglion are born (i.e.
they have undergone their last cell division) at about the same
time. [A staging scheme (Reynolds et al., 1998a) divides
development into percentage of embryonic development or
%ED. At 20 8C, leech development takes 30 days; it is faster at
higher temperatures and slower at lower ones.] Between 35 and
40% ED, all the neurons in midbody ganglia start growing
processes at the same time. They grow their longest process first
(i.e. sensory and MNs grow processes to the periphery, and INs
grow their interganglionic processes), which are destined to
become axons. At about 50% ED the central processes, which
will become the sites of synaptic contacts, appear. The first
signs of synaptic interactions appear at about 52% ED, at the
same time as coordinated behaviors – both spontaneous and
evoked – become visible (Reynolds et al., 1998b).
Simple, local behaviors, such as shortening and the
precursor to local bending, are seen earliest, followed by
crawling and swimming, which appear at about 70% ED
(Fig. 30A). Initially, the behaviors are weak and labile,
becoming adult-like over the course of 10–15% ED after they
first appear. Local bending develops in two stages. At about
50% of ED (about two weeks after fertilized eggs are deposited
into a cocoon), touching a mid-body segment causes it to
contract all around, a behavior termed ‘‘circumferential
indentation’’ (Fig. 30B). Over the course of a week, this
indentation gives way to adult-like local bending. The onset of
circumferential indentation coincides with the formation of the
initial contacts among the MNs, which are exclusively
electrical (Marin-Burgin et al., 2005). Local bending appears
as the chemical connections – particularly the inhibitory ones –
are established. (Some of the electrical connections disappear,
but many of them are retained into adulthood; they are largely
overridden by chemical inputs during adult local bending.)
Fig. 30. Development of behaviors in the embryonic leech. (A) Summary plot
of behaviors, both spontaneous and elicited, during embryonic time (ET). Along
the line are the percentages of embryonic time (100% takes about 30 days at
20 8C.) Above the time line are the earliest times that the major behaviors, either
spontaneous or elicited, can be seen. (The components of crawling, including
‘‘elongation’’ appear over an extended period of time.) Below the line are shown
the onset times of electrogenesis, electrical synaptic connections, and chemical
synapses in MNs. (B) Switch from circumferential indentation to local bending
during embryogenesis. Two different behaviors – circumferential indentation
and local bending – can be elicited by light touch of the skin in the middle of the
animal starting just after 50% ET. Circumferential indentation begins first, then
wanes in probability as local bending becomes more prominent.
Hence, the progression from circumferential indentation to
local bending results from the establishment of electrical
connections first, followed chemical ones, among the relevant
neurons. Swimming starts later in development, at about 62%
ED, with the appearance of alternating dorsal and ventral
flexions (French et al., 2005). At this stage, the peaks and
troughs do not pass through the body, i.e. there are no
undulations that propel the animal through the water. Over the
course of a week, the front-to-back undulations appear and
become more pronounced, so that swimming is adult-like by
the end of embryonic life. Leech neurons are sufficiently large
and accessible to allow intracellular recording from the genesis
of electrogenic and synaptic contacts (Marin-Burgin et al.,
2005), so it should be possible to determine how these
behaviorally relevant neuronal circuits are formed during
embryogenesis.
4. Conclusion
This overview of the neuronal mechanisms underlying six
distinct behaviors in the medicinal leech illuminates the utility
of this animal for gaining insights into rhythmic movements,
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sensory feedback, neuromodulatory control, interactions
among behaviors, and behavioral choice. The gaps in our
understanding of the biophysical and developmental bases of
these behaviors, in the face of so much existing information,
make the leech a prime contender for further study. With the
function of about one-third of its neurons already identified, it is
not unrealistic to aim for a description of the functional roles of
all neurons in the leech nerve cord. Far-ranging ramifications of
such an accomplishment would include a deeper understanding
of electrical coupling and its role in behavioral circuits, of the
functions of non-spiking versus spiking interactions in sensory–
motor systems, generation of a wide range of animal behaviors
from a limited set of neurons participating in overlapping
functions, and the neuronal ontology of behaviors. The leech
has served an important model animal for neuroscience since
the late 19th century (Mann, 1962; Sawyer, 1986). It will
clearly continue to play that role, for even now research papers
concerning leech biology are published at the rate of about 100
per annum.
Acknowledgements
Funding was provided by NIH grants MH43396 and
NS35336, and a grant from Microsoft Research Labs (to
WBK); NSF grant IBN-0110607 and NIH grant NIMHMH63855 (to WOF); and NIH grant NS24072 (to RLC). We
express our deep gratitude to numerous students and colleagues
who collaborated with us over a span of more than three decades.
Without their expert and active participation in the research
described here, this review would have been much shorter. We are
especially grateful to Prof. Gunther Stent, under whose tutelage
the neuronal circuits described here saw first light. Finally, we
thank Courtney Milianes for expert editorial assistance.
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