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Fundamental Neuroscience (2nd Edition): Section V. MOTOR SYSTEMS
Chapter 32: Cerebellum
James C. Houk and Enrico Mugnaini
The cerebellum (Latin for “little brain”) is a strategic part of the nervous system. It
contains more neurons and circuitry than all the remainder of the brain, and it packs this
into only 10% of total brain weight. It covers the dorsal surface of the brainstem and
comprises the largest part of the hindbrain. The cerebellum’s important function is to
regulate neural signals in other parts of the brain, and it does this through loops of
interaction. Currently, we know most about its regulatory actions on the populations of
neurons that command movement and posture. Although the cerebellum is not necessary
for the initiation of motion, movements become erratic in their size and direction when it
is damaged – a symptom that clinicians call dysmetria. The cerebellum is an important
site of motor learning, in addition to movement execution. The size of the cerebellum in
mammals parallels the evolution of the cerebral cortex, and the newest regions of the
cerebellum appear to regulate higher cerebral processes for motor planning, cognition and
problem solving.
OVERVIEW
The cerebellum has lobes and lobules.
The cerebellum consists of three paired longitudinal subdivisions, the medial (or vermal)
zone, the intermediate zone, and the lateral (or hemispheral) zones. The vermis (from the
Latin worm), is a narrow structure that straddles the midline. On the cerebellar surface
(Fig. 32.1A), the borders between the vermis and the hemispheres are demarcated by
shallow indentations occupied by small veins. The medial part of the hemispheres
bordering the vermis are called the intermediate zones. These zones are distinguished
from the rest of the hemispheres primarily by their fiber connections.
Deep transverse fissures subdivide the cerebellum rostrocaudally into three lobes, the
anterior lobe, the posterior lobe, and the flocculonodular lobe (Figs. 32.1B & 32.6). The
anterior and posterior lobe together form the corpus cerebelli. By contrast with the
cerebral lobes, the cerebellar lobes are continuous across the midline. Shallow fissures
subdivide further the cerebellar lobes into lobules. Each lobule consists of thin parallel
folds called folia (leaves), which run roughly transverse to the long axis of the body. The
lobules that appear in sagittal section are summarized in Box 32.1.
The number of folia in different lobules varies, but each folium contains a white matter
core. In sagittal section, the core appears as arboreal branches departing from the roof of
the fourth ventricle (Fig. 32.1C). Embedded into the deep white matter core on each side
of the midline are the cerebellar nuclei (CN) -- the medial nucleus, which projects mainly
to nuclei in the lower brainstem and the spinal cord, the interpositus nucleus, which
targets the midbrain, and the lateral nucleus, which projects to the thalamus and on to the
cerebral cortex. The interpositus nucleus consists of two subdivisions, which are usually
referred to as anterior and posterior interpositus nuclei. In the human cerebellum, the
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corresponding four nuclei (Fig. 32.1A) are classically termed fastigial, globose,
emboliform, and dentate because of their morphological appearance. These
nomenclatures are sometimes used interchangeably. Beneath the cerebellum is the
vestibular nuclear complex, some divisions of which receive input from the cerebellar
cortex and therefore bear analogy with the CN.
The cerebellum is connected to the brainstem bilaterally by three cerebellar peduncles
(Fig. 32.1D), the superior, middle, and inferior cerebellar peduncles (classically termed
brachium conjunctivum, brachium pontis, and corpus restiforme), that carry information
to and from the cerebellum. The superior cerebellar peduncle is mostly efferent and
contains fibers from the CN to brainstem, red nucleus, hypothalamus, and thalamus; the
middle cerebellar peduncle contains exclusively afferents from the contralateral pontine
nuclei; the inferior cerebellar peduncle contains afferent fibers from the brainstem and the
spinal cord, as well as cerebellar efferent fibers to the vestibular nuclei.
In humans, the fibers of the superior, middle, and inferior cerebellar peduncles number
approximately 0.8 million, 20 million, and 0.5 million. Interestingly, the number of fibers
in the massive middle cerebellar peduncle (cerebrocerebellar afferents or pontocerebellar
afferents) roughly equals that of the cerebral peduncle, which carries the input of the
cerebral cortex to the brainstem and spinal cord and, via pons, back to the cerebellum.
Box 32.1: NOMENCLATURE FOR CEREBELLAR LOBULUES
In the classical literature, the cerebellar lobules were designated by descriptive Latin
names, denoting their features in humans and other mammals. Although the Latin
nomenclature is still in use, a later, practical Roman numeral system has facilitated
comparative neurology. According to this system, lobules are numbered I-X beginning
at the inferior anterior vermis and ending at the inferior posterior vermis. In most
mammals, each lobule contains a number of folia. The anterior lobe consists of lobules IV, the posterior lobe of lobules IV-IX, and the flocculonodular lobe of lobulus X.
Lobulation is fairly consistent across individuals of the same species and extends with
few exceptions across all mammalian species, despite great variation in hemispheric
development (Brodal, 1969).
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Fig. 32.1 Gross features of human cerebellum.
(A) Dorsal view of the cerebellum and brain stem. Part of the right hemisphere has been
cut out to show the cerebellar peduncles. Profiles of the four cerebellar nuclei are
projected onto the cerebellar surface to indicate their position.
(B) Ventral view of the cerebellum detached from the brain stem.
(C) Midsagittal cut through the cerebellum and brain stem, showing the white matter
entering the vermal lobules.
Left side view of the lower part of the brain stem after removal of the cerebellum to
highlight routes of efferent and afferent inputs. Direction and thickness of arrows indicate
directions and relative numbers of fibers in the three cerebellar peduncles. Cranial nerves
are indicated by Roman numerals. (A, B, and C are adapted from Nieuwenhuis et al.,
1988 and Kandel, E. R., Schwartz, J.H., and Jessel, T.M., Principles of Neural Science,
Norwalk, CT: Appleton & Lange, 1991, with permission. D is adapted from Brodal, A.,
Neurological Anatomy. New York: Oxford University Press,1981, with permission.)
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The microcircuitry is largely homogeneous across the surface.
The cerebellar cortex is a three-layered, folded sheet of gray matter, only 1 mm thick and
largely homogeneous throughout the whole cerebellum. It’s unique anisotropic layout can
be appreciated by comparing the simplified transverse and sagittal views of the
microcircuitry provided in Figs. 32.2 32.3. [The full 3-D complexity is elaborated later
(Fig. 32.9).] The three layers of this cortex are named - beginning from the pial surface the molecular layer, the Purkinje cell layer, and the granular layer. The cerebellar cortex
contains: (1) a single type of efferent neuron, the Purkinje cells (PCs), which are
inhibitory and project to the cerebellar nucleus (CN) and to the vestibular nucleus; and
(2) five main classes of interneuron, three of which are inhibitory (stellate cells, basket
cells, and Golgi cells) and two are excitatory (granule cells and unipolar brush cells). The
cortex receives two main types of afferents (illustrated in color in Fig. 32.3) -- the mossy
fibers (MFs), show blue, and the climbing fibers (CFs), shown red, are both excitatory.
The molecular layer (labeled a in Fig. 2) is cell-poor; it primarily contains PC dendrites
and their afferents - the parallel fibers (PFs) and the climbing fibers (but also the
inhibitory stellate and basket cells that are left out of Figs 32.2 & 32.3). The Purkinje cell
layer is only one-cell-thick, but it is well marked by its large PCs (> 50 µm in large
mammals). The granular layer is extremely cell-rich. It receives the MFs, which form
excitatory glutamatergic synapses on the granule cells, unipolar brush cells, and Golgi
cells.
The dendritic tree of the PC arises from the apex of the cell body and branches profusely
in the molecular layer (Fig. 32.3). It is fan-shaped (compare the PC’s appearance in
transverse and sagittal views), like a tree trained to grow flat against a railing, and
extends in a plane perpendicular to the main axis of the folium (usually the parasagittal
plane). The proximal branches of the Purkinje cell dendrite appear smooth, although they
are provided with scattered spines (all in contact with a single CF). By contrast, the distal
dendritic branches are covered with spines (spiny branchlets), most of which establish
contact with PFs running perpendicular to the Purkinje tree (along the course of the
folium as illustrated by the horizontal lines in Fig. 32.2). In large mammals, each
Purkinje tree bears over 200,000 synaptic spines. The PC axon, after giving off some
recurrent collaterals, enters the white matter and terminates in one of the cerebellar
nuclei, or in the vestibular nucleus. Although PCs display some chemical heterogeneity,
they all release the inhibitory neurotransmitter GABA. The output of the cerebellar
cortex, therefore, is purely inhibitory. This output is regulated by two prominent
excitatory influences, the MF Æ PF pathway and direct CF inputs. PCs also receive feedforward inhibition from basket and stellate cells and neuromodulatory inputs from
noradrenergic, cholinergic and serotonergic neurons in the brainstem.
The granular layer contains an enormous number (billions) of granule cells, which are the
smallest neurons found in the brain (Fig. 32.2). Their spherical cell bodies form densely
packed clusters, which are separated by islands of neuropil termed cerebellar glomeruli. It
is often stated that cerebellar granule cells outnumber the sum of all the other neurons in
the central nervous system. The granule cell emits four or five thin dendrites that
terminate in claw-like protrusions into the glomeruli. Their axons ascend into the
molecular layer where they bifurcate to form the PFs, which may reach a length of 6-8
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mm. The granule cell axon is provided with presynaptic varicosities along it’s full course.
Varicosities of the ascending granule cell axon terminate on Golgi cells in the granular
layer and on PC spiny branchlets in the molecular layer. Most of the varicosities are
along the PF and innervate the spiny branchlets of the PCs and the dendrites of the
cerebellar interneurons that the PF passes along its course.
Fig. 32.2 Transverse view of microcircuity.
Schematic section of an ideal short cerebellar folium cut parallel to its course, based on
the Golgi impregnation method. (a) Molecular layer with parallel fibers (PFs); (b)
Purkinje cell (PC) layer; (c) granular layer; (d) white matter. Stained Purkine cell
dendrites, which are oriented flat perpendicularly to the direction of the folium, appear as
cypress trees. PFs, which are formed by granule cell axons after a T-division, synapse
with a large number of PC dendrites which they traverse along their course. Mossy fiber
terminals, which provide input to granule cells, are not shown. (Adapted from Cajal, S.
R., Textura del Sistema Nervioso del Hombre y de los Vertebrados. Madrid: Imprenta y
Libreria de Nicolas Moya, 1899).
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Fig. 32.3 Saggital view of microcircuitry.
Schematic illustration of a folium in parasaggittal section, with three PCs (black)
illustrated in S. Ramon y Cajal’s fashion. The PCs send their inhibitory axon to a single
excitatory cerebellar nucleus (CN) neuron and are innervated by branches of a single
climbing fiber (CF) (red). Two mossy fibers (MFs) (purple) branch in the granular layer
forming terminals that innervate granule cells within glomeruli (not shown, but see Fig.
9). PFs, which run parallel to the direction of the folium, are represented by purple dots.
The CF and one of the MFs give off collaterals, which form terminals (red and purple
knobs) synapsing with the CN neuron. The CN neuron projects its axon (red arrow) to
targets outside the cerebellum.
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Neural signals are processed according to a modular scheme.
From the signal processing perspective, the two main divisions of the cerebellum are
cerebellar cortex and cerebellar nucleus (Fig. 32.4). The cerebellar cortex is specialized
for processing extremely large amounts of information about the states of body parts, of
objects around us, and of ongoing brain activities. This variety of state information is
conveyed to the cerebellum by its numerous MF inputs. The state of body parts comes
from our kinesthetic receptors, which signal the forces, lengths and velocities of the many
muscles throughout the body and the strain and motion of the skeletal joints. The state of
the world is monitored by our tactile receptors, which sense contact forces, shears and
locations of nearby objects, and by our visual and auditory systems, which analyze the
properties of more distant objects in the world around us. The internal state of our brain is
monitored by projections from neurons in brain areas that deal with perceptions, goals,
motor commands and problem solving. The large array of state information is called a
MF state vector (vector is simply a concise way of referring to a set of variables).
The MF state vector is diversified further by the interneuronal circuit in the granular layer
so as to produce a PF state vector, which functions as an enormous, highly diverse array
of potential input to a large number of PCs. Under the influence of training signals
conveyed by CFs to the molecular layer (Fig. 32.4), PCs learn to detect specific patterns
in their state vectors. This allows the PCs to classify the many patterns of state that occur
at different times and under different contexts. This specialized neuronal architecture
functions as a remarkable learning machine.
The pattern classifications detected by the PC's are transmitted to CN neurons via
inhibitory projections (open arrows in Fig. 32.4). As a consequence of the PC to CN
projections being inhibitory, the cerebellar cortex is not well-suited for initiating nuclear
cell activity directly. However, the inhibitory input from PCs is indeed potent and is
highly effective in regulating the spatial and temporal patterns of CN discharge promoted
by other causes. CN discharge is promoted both by the intrinsic properties of the neurons
and by excitatory synaptic input, especially that coming from collaterals of select MFs.
The CN serves as the final common output from the cerebellum. The CN projects to
neuronal output populations that are located in different regions of the brain, and these
output neurons send collaterals that loop back onto the same region of the cerebellum
(part of this loop is labeled Attractor Network in Fig. 32.4). These loops effectively bind
populations of neurons that are located in many other parts of the brain to the regulatory
operations of the cerebellum.
The signal processing scheme outlined above is organized in a modular fashion. Different
zones of the CN receive their PC input from different parasaggital zones in the cerebellar
cortex. Zones in the vermis and flocculus regulate the accuracy of trunk, leg, head and
eye movements -- movements that are critical for the control of posture, locomotion and
gaze (Chapter 30). Intermediate zones regulate the accuracy of movements that we call
voluntary -- the reaching and grasping movements that we use to obtain and manipulate
objects with our hands and arms (Chapter 30). The most lateral zones, in the
hemispheres, regulate higher aspects of behavior. The enormously expanded hemispheres
in humans plan complex movements, regulate cognition and engage in problem solving.
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Fig. 32.4 Modular signal processing scheme.
A variety of state information arrives to this schematized module of the cerebellum via its
MFs. These signals are diversified further in the granular layer to present (in the
molecular layer) enormous arrays of potential PF input to many PCs (only 3 of which are
illustrated). Under the training influence of the CFs, the PCs learn to detect specific
patterns when they occur in their MFÆPF input. The inhibitory PCÆCN projections then
function to regulate the spatiotemporal pattern of activity in an Attractor Network formed
by a group of cells in CN that connect reciprocally with an Output Population (OP) of
neurons in another part of the brain. State transitions of the attractor network, eg. from
relative quiescence to intense activity, can be initiated by one of the Diverse Inputs to the
OP, under the regulatory influence of the inhibitory projection sent from the basal
ganglia. When the attractor network is in its active state, the cerebellar cortex can shape
OP activity into a useful spatiotemporal pattern of output. In a nutshell, a cerebellar
module learns to use it’s complex state-related input to control the dynamics of the output
population that it targets.
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Voluntary motor commands exemplify modular signal processing.
To illustrate modular signal processing more specifically, we focus on the intermediate
cerebellum and its regulation of voluntary movement commands (Fig. 32.5), since this is
a relatively well understood example of the generic modular processing diagrammed in
Figure 32.4 (Houk, 2001). In Figure 32.5, the module regulating voluntary motor
commands is highlighted, both in blue (Intermediate Cerebellar Cortex) and in red (the
Limb Premotor Network). The limb premotor network is an example of the Attractor
Network diagramed in Figure 32.4. It is comprised of an elaborate set of interconnections
between CN, red nucleus and motor cortex. Interpositus neurons project to the red
nucleus directly, and some project on to the motor cortex by way of the ventral thalamus.
Most of the input to the motor cortex, via thalamus, derives from CN neurons in a
relatively small dorsal zone of dentate. Both the red nucleus and the motor cortex
transmit voluntary movement commands to motor neurons in the spinal cord and
brainstem via their output fibers (Chapter 30), but they also send collaterals to
precerebellar nuclei, the pons and the lateral reticular nucleus (LRN), that originate MFs
which loop back to the intermediate cerebellum. These copies of motor commands
(efference copy signals) inform both the cerebellar cortex and the CN about actions
currently being commanded. Figure 32.5 also illustrates connectivity with other areas of
the cerebral cortex and basal ganglia.
The MF collaterals that loop back to intermediate nuclear cells close the recurrent
pathways of the attractor network that was illustrated generically in Figure 32.4; the
concept of attractor neural networks is elaborated in Box 32.2. The resultant positive
feedback in the limb premotor version of an attractor network (red in Fig. 32.5) appears
to be an important driving force for the amplification of motor command generation.
Additional neurons need to be recruited, while the activity in already recruited neurons
needs to be amplified in intensity and in duration, so as to create the population of intense
burst discharge that comprises a composite voluntary motor command. When positive
feedback is sufficiently strong, it promotes the regenerative activity that is needed for
amplification and for sustaining discharge in nuclear cells in the face of the potent
inhibition sent from PCs. This regenerative activity can be initiated by any of the Diverse
Inputs (Fig. 32.4) sent to motor cortex or to red nucleus, such as the inputs produced by
sensory cues. This raises the question of how the initiation process is regulated. Initiation
of motor commands appears to be regulated by inhibitory inputs sent from the basal
ganglia (Chapter 31), as shown on the left side of Figure 32.5. This influence amounts to
a disinhibition in the motor cortex, which allows other cortical inputs to initiate
regenerative activity in the cortical-cerebellar loop.
Box 32.2: ATTRACTOR NEURAL NETWORKS
Our quest to identify and understand the brain mechanisms responsible for the complex
dynamics observed in neuronal assemblies has found a powerful tool in the use of neural
network models. These models are based on relatively simple nonlinear units that capture
only the most basic properties of individual neurons, such as synaptic integration of
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inputs and nonlinear modulation of the firing rate. The rich dynamical behavior observed
at the network level is due to a high degree of connectivity, and it is through the
organization of this connectivity in specific circuits that functionally and computationally
useful dynamical properties can be selected and stabilized. Complexity is thus a
collective property whose source is to be found in connectivity.
Two basic types of network connectivity are to be distinguished: layered and recurrent.
Layered networks, based on forward maps between subsequent layers, implement
arbitrarily complex input-output maps. Recurrent networks, of particular interest here,
incorporate feedback loops to sustain iterative dynamical processes based on the
continuous update of network state. For a recurrent network composed of N neurons, the
state of the network is specified through an array of N numbers representing the firing
rates of the N neurons. The state of the network at any time can be visualized as a point in
an N-dimensional space, where each coordinate axis corresponds to the firing rate of a
specific neuron. As the state of each neuron changes with time, the point that represents
the state of the network moves in this N-dimensional space of firing rates. The trajectory
described by this multidimensional point allows us to visualize the dynamical evolution
of the network.
Trajectories that keep on moving about and visit more and more regions of network state
space, never settling anywhere, correspond to a type of dynamical behavior called
ergodic. More interesting dynamical behavior, associated with persistence, arises when
trajectories are attracted to special regions of state space. These regions are labelled as
attractors, and the recurrent networks whose dynamics converge to them are called
attractor neural networks. The attractors are of three types: fixed points, limit cycles, and
strange attractors. Here we are interested in fixed point attractors. Each one of these
special points controls a specific region of state space, its basin of attraction. If a
trajectory starts at any point within this basin, it will go towards the corresponding fixed
point, where it will settle. The basin of attraction thus defines a set of network states that
will evolve dynamically until they reach the attractor state. The attractor is called a fixed
point because once the network reaches this special state, it remains there. The fixed
point is stable because network states that are close to it flow into it. It is as if the point
that represents the state of the network were a ball frictionally gliding on a landscape of
hills and valleys. The ball will move towards lower points until it reaches the bottom of
the valley that is strictly downhill from its initial position. Once the ball reaches this
minimum, it will stay there. The attractor fixed points thus correspond to the network
states at the bottom of the valleys.
Fixed point attractors provide a mechanism for the implementation of a set of motor
commands in the limb premotor network (Fig. 32.5). The cerebello-thalamocorticalponto-cerebellar excitatory loop acts as a recurrent network. A computational model (Hua
& Houk, 1997) has established that pathways around the loop provide a mechanism for
each one of these modules to develop effective lateral connections which, in the case of
the cerebellar nucleus and the thalamo-cortical circuits, take the form of a banded
diagonal excitatory matrix. This type of connectivity leads to dynamical behavior
controlled by the existence of two fixed points: a low activity state in which all neurons
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fire at very low baseline rates, and a high activity state in which all neurons would fire at
very high rates. In the absence of further inputs, the low activity state is an unstable fixed
point and the high activity state is the stable fixed point, the attractor. But the activity of
the cerebellar nucleus neurons is strongly modulated by inhibitory projections from the
cerebellar Purkinje cells. This modulation introduces two important modifications in the
dynamical behavior of the recurrent network. First, the low activity state is stabilized by
the concerted inhibitory action of the Purkinje cells. When all of them are firing, activity
in the loop is suppressed. The low activity state thus acquires a small basin of attraction.
Second, and crucial to the ability of the limb premotor neuron to encode a variety of
motor commands, the high activity state is not a uniform state in which all units fire at
high frequency: the disinhibition of a subset of cerebellar nucleus neurons selected
through the inactivation of specific Purkinje cells results in a specific pattern of activity
that involves the thalamo-cortical circuits and the pons. The recurrent network sustains
the high frequency firing of a subset of neurons while the others remain quiescent or fire
at low frequency, baseline levels.
The precise location of the high activity attractor in network state space depends on the
inputs to the cerebellar nucleus provided by the Purkinje cells. Different attractor
locations represent different subpopulations of neurons involved in high frequency firing;
each of these patterns of network activity encodes for a specific motion as it gets
exported from the motor cortex into the spinal cord and the brainstem. Motion initiation
requires a transition from the low activity attractor to the high activity attractor; the
mechanism for this transition is the activation of a subset of motor cortical neurons due to
sensory input from other cortical areas. The activity of the cerebello-thalamocorticalponto-cerebellar loop can thus be understood as resulting from the competition between a
low activity fixed point with a small basin of attraction and a high activity fixed point
with a large basin of attraction. The precise network state associated with the high
activity fixed point is selected by the Purkinje cells projections onto the cerebellar
nucleus; different patterns of activity encode different sets of motor commands.
Sara A. Solla
Hua, S. E. and Houk, J. C. (1997). “Cerebellar guidance of premotor network
development and sensorimotor learning.” Learning & Memory 4: 63-76.
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Fig. 32.5 Application of the modular signal processing scheme to the limb premotor
network.
Diverse sensory inputs, or inputs from the supplementary motor area (SMA) or the
premotor cortex (PM), can activate neurons in the motor cortex (M1) or magnocellular
red nucleus (RNm). The spread of this activity through the limb premotor network is
regulated by inhibitory input from PCs in the intermediate cerebellar cortex, so as to
produce a composite voluntary motor command appropriate for controlling the motion of
the limb. Closed arrows designate predominantly excitatory projections whereas open
arrows designate predominantly inhibitory projections; IP, interpositus nucleus; dD,
dorsal zone of dentate nucleus; VL, ventrolateral thalamus; LRN, lateral reticular
nucleus; VLo, pars oralis of VL; vGPi, ventral zone of globus pallidus pars interna; GPe,
globus pallidus pars externa; ST, subthalamic nucleus.
Amplification in the limb premotor attractor network insures that sufficient motor neuron
activity is ultimately achieved, so as to move the limb in appropriate directions and to
open the hand in preparation for closing around an object that needs to be manipulated.
Of course the composite voluntary command needs also to be shaped appropriately, so
that the individual commands contribute to the overall accuracy of reach and grasp. To
achieve this, the individual commands need to have appropriate intensities and durations
of discharge, which is the critical refinement function of well-controlled and wellcoordinated arrays of potent PC inhibitory input to the limb premotor attractor network
(Miller et al. 2002).
How does the cerebellar cortex learn to perform this complex regulatory function? There
is a growing body of evidence, reviewed in a later section, that PCs learn under the
guidance of an array of training signals that are transmitted to the cerebellar cortex by
CFs. Our presently limited information about climbing fibers is generally consistent with
the concept that they transmit relatively specific error information to those PCs that are
capable of reducing particular movement errors (Houk et al. 1996; Simpson et al. 1996).
Since each PC is innervated by only a single CF, its training information can be quite
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specific. In contrast, the PC receives about two hundred thousand inputs conveying state
information from its MF Æ PF system. The PF synapses that were activating the PC just
before the climbing fiber discharged are weakened. This learning rule utilizes a special
mechanism for synaptic plasticity that is discussed in a later section.
Computational models have demonstrated that the learning paradigm outlined above is
capable of training PCs to control complex movements accurately, even in the presence
of the substantial time delays that occur in the neural pathways that control and monitor a
movement (Barto et al. 1999). Since the capacity for overcoming time delays requires an
ability to predict, one can surmise that the intermediate cerebellum may be capable of
functioning as a predictive controller of the spatiotemporal patterns of neural activity in
the limb premotor network. Similarly, other parts of the cerebellum should be capable of
predictively controlling other output populations. Predictive regulation of neuronal
populations is an extremely valuable tool for the postural, gaze and locomotor functions
of the medial cerebellum, for the voluntary movement functions of the intermediate
cerebellum, and for the movement planning and cognitive functions of the cerebellar
hemispheres.
Summary
The cerebellum is divided into many regional zones. Although each zone receives
different inputs and projects to neuronal populations in different parts of the brain, the
microcircuitry is similar across the entire cerebellum, suggesting that signal processing
operations are modular. The cerebellar contribution to the regulation of voluntary motor
commands was used here to introduce modular signal processing principles.
ORGANIZATION OF SIGNAL PROCESSING MODULES
Mossy fibers bring different kinds of state information to different modules.
Mossy fibers originate from: (i) centers, termed precerebellar nuclei, that project
exclusively or nearly exclusively to the cerebellum; and (ii) centers that send collaterals
to the cerebellum in addition to having major projections outside the cerebellum. The
major precerebellar nuclei are the basilar pontine nuclei, the lateral reticular nucleus, and
the reticular tegmental pontine nucleus, and the other major centers are the vestibular
nuclei, the external cuneate nucleus, and groups of cells in lamina VII of the spinal cord
(Clarke’s column and border cells). MFs carry diverse state information about the
periphery and other brain centers. Because the MFs generally originate from secondorder sensory neurons, some processing of afferent information occurs before that
information is sent to the cerebellum.
MFs carrying state information from different parts of the nervous system project to
different parts of the cerebellum (Fig. 32.6). The anterior and posterior portions of the
vermis and the adjacent hemispheral regions are primarily innervated by fibers from the
spinal cord and are termed the spinocerebellum. The lateral portions of the hemispheres
and the central folia of the vermis (the visual vermal area: folium and tuber vermis) are
primarily innervated by fibers from the basilar pontine nuclei and are termed the
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pontocerebellum or cerebrocerebellum. The pontine nucleus has an elaborate
representation of input from widespread areas of the cerebral cortex (Brodal & Bjaalie,
1992). The flocculonodular lobe is primarily innervated by fibers from the vestibular
ganglion and from the vestibular nuclei and is termed the vestibulocerebellum. Important
MF systems, arising in the reticular formation and the nucleus reticularis tegmenti pontis,
provide the vestibulocerebellum with optokinetic information. The distribution of spinal,
basilar pontine, and vestibular MF systems is in accord with functional subdivisions of
the feline and primate cerebellum based on different behavioral abnormalities that result
when each was each part is damaged or subjected to pharmacological blockade (Voogd &
Glickstein, 1998).
Upon reaching the cerebellum, the MFs branch extensively. They generally distribute
bilaterally, with either an ipsilateral or contralateral predominance. They terminate either
in multiple, symmetrically arranged, parasagittal zones or in patches. The few studies of
the subject indicate that different mossy fiber systems remain segregated in the granular
layer. A detailed study in the rat showed that each patch contains a representation of a
small body part, but the same body part can have multiple representations (Bower et al.
1981). Neighboring patches can have representation of different body parts that are
functionally related, for example, perioral region and paw. The patchy pattern is called
fractured somatotopy. Because the mossy fiber–granule cell-Purkinje cell pathway is a
widely divergent system, which may influence Purkinje cells belonging to different zones
in different regions of the cerebellum more or less simultaneously, each Purkinje cell may
receive information about sensory conditions, internal states, external states, and the
plans of the organism.
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Fig. 32.6 Organization of mossy fiber input.
Schematic representation of the mossy fiber input to the anterior, posterior, and
flocculonodular cerebellar lobes, that roughly define the spinocerebellar,
cerebrocerebellar (pontocerebellar), and vestibulocerebellar regions. (Adapted from Dow,
R.S. 1942 The evolution and anatomy of the cerebellum. Biol. Rev. 17: 179-220)
Climbing fibers are organized in parasagittal zones.
All CFs arise from the inferior olive, which is a complex of larger and smaller subnuclei
located in the ventral medulla oblongata (Fig. 32.7). The largest of these subnuclei, the
principal olive is greatly expanded in humans and is configured as a folded sheet of cells,
resembling the expanded and folded lateral cerebellar nucleus with which it is connected.
The olivocerebellar projection is strictly modular (Armstrong & Hawkes, 2000; Voogd &
Glickstein, 1998). Subdivisions of the inferior olive project to specific subdivisions of the
cerebellar and vestibular nuclei that underlie 0.5 mm wide, parasagittally oriented zones
of the cerebellar cortex. The same subdivisions of the cerebellar and vestibular nuclei
loop back to the subnuclei of the inferior olive from which the olivocerebellar projection
originated (note the matching colors in Fig. 32.7). Moreover, the projections from the
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cerebellar cortex to the cerebellar and vestibular nuclei and the projections from the
cerebellar and vestibular nuclei to the inferior olive form closely corresponding loops.
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Fig. 32.7 Organization of climbing fiber input and cerebellar output zones.
Diagram of the zonal organization in the corticonuclear and olivocerebellar projections in
the cat. (a) the flattened cerebellar cortex with the parasagittal zones; (b) the cerebellar
and vestibular nuclei; and (c) profile of the inferior olive in the horizontal plane. The
longitudinal corticonuclear and olivocerebellar projection zones are indicated with
capitals (A, X, B, C1-3, D1,2). The zones, their target nuclei and the subnuclei of the
inferior olive which project to these zones are indicated with the same colors. The
diagram applies equally to the monkey cerebellum, with the exception of the floccular
zones, the most medial one of which is lacking in the monkey. Asterisks: areas without
cortex. Abbreviations: ANS, ansiform lobule; ANT, anterior lobe; D, dorsomedial cell
column; Dc, caudal dentate nucleus; DC, dorsal cap; dl, dorsal leaf of principal olive;
FLO, flocculus; I, intermediate cell group; IA, anterior interpositus nucleus; IP, posterior
interpositus nucleus; LV, lateral vestibular nucleus; MAO, medial accessory olive; N,
nodulus; PFLD, dorsal paraflocculus; PFLV, ventral paraflocculus; PMD, paramedian
lobule; PO, principal nucleus of the inferior olive; PY, pyramis; SI, lobulus simplex; UV,
uvula; vl, ventral leaf of principal olive; VLO, ventrolateral outgrowth; VII, lobule VII.
(Courtesy of Voogd, J., 2001).
Specialized zones of CF projection to the cerebellar cortex have been identified across
mammals. The vermal cerebellar cortical zone comprises three parasagittal projection
zones, termed A, X and B; the intermediate zone comprises parasagittal zones C1, C2, and
C3; and the hemispheral zone comprises parasaggittal zones D1 and D2. Several subnuclei
of the inferior olive contain a detailed somatotopic map, and this somatotopy is
reproduced in the corresponding climbing fiber zone as a pattern of so-called microzones.
The receptive fields of PC responses to MF input appear to be specifically influenced by
the receptive fields of their CFs (Ekerot & Jörntell, 2001). Contrary to the systematic
divergence in the MF-PF system, the climbing fiber system is a highly focused onto
microzones, and each microzone projects to a small cluster of nuclear neurons.
The olivary axons cross the midline in the ventral medulla at the level of their site of
origin. After entering the cerebellum, an individual climbing fiber leaves collaterals in the
cerebellar nucleus that provide the reciprocal nucleo-olivary projection to the parent
olivary neuron, and then ascends towards the cortex branching repeatedly in the sagittal
plane to make contact with up to 10 Purkinje cells. Each Purkinje cell, however, receives
input from only one climbing fiber. With more than a thousand synapses of a single fiber
with an individual cell, the climbing fiber-Purkinje cell pathway represents an example of
a giant synapse and has powerful excitatory and metabolic effects.
The inferior olive shows several unifying structural features: 1) it contains a
homogeneous population of spiny projection neurons and rare interneurons; 2) within a
subnucleus, all projection neurons are electrically coupled to each other by gap junctions,
most of which link together dendritic spines and may serve to share postsynaptic
currents; 3) all olivary projection neurons use glutamate as a neurotransmitter and
corticotropin releasing factor (CRF) as a modulatory neuropeptide; 4) all olivary
projection neurons receive excitatory and inhibitory inputs, mostly on the spines and
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stems of peripheral dendrites; 5) all olivary subnuclei receive a strong GABAergic
innervation. CRF is generally expressed by neurons involved in stress signaling
throughout the brain.
Outflow engages motor, autonomic and cognitive parts of the brain.
The cerebellar outflow ultimately reaches all motor nuclei (Brodal, 1998), structures
within the autonomic nervous system (Dietrichs et al. 1994), and many areas of the
cerebral cortex (Middleton & Strick, 1998), with topically organized connections. The
outflow from neurons occupying discrete subdivisions of the cerebellar nuclei targets
specific neuronal populations in the thalamus, hypothalamus, red nucleus, tectum, pons,
medulla, and cervical spinal cord. The outflow also loops back to the cerebellum via
several nuclei, primarily the pontine tegmental reticular nucleus, the basilar pontine
nuclei, the lateral reticular nucleus, and the inferior olive.
Individual excitatory neurons residing in each cerebellar nucleus have axons that form
discrete patches of synaptic terminals in a primary target nucleus, and the axons also
often send collaterals to other target nuclei. Collaterals of the individual cerebellar
nuclear neurons are hypothesized to terminate on functionally congruent groups of
neurons in the target nuclei. The functional congruency would be achieved by
stabilization of effectual connections during maturation of the sensory-motor circuits.
The small inhibitory neurons of the cerebellar nuclei have axons projecting in a similarly
discrete manner, but they do not collateralize; they project to specific regions of the
inferior olive, the source of all CF input to PCs. It is generally assumed, therefore, that
cerebellar connections are organized in a complex, but detailed topical order.
The zonal organization of the cortical maps is well correlated with physiological findings.
In the vestibulocerebellum different zones exert a plane-specific control of the external
muscles of the eye. A similar specification may be present in the zones of the corpus
cerebelli, with the A zone regulating the inhibitory vestibulospinal tracts, the B zone
regulating the excitatory lateral vestibulospinal tract and the intermediate C1 C2 and C3
zones regulating the rubrospinal system . A portion of the A zone in the central vermis
(visual vermis: folium and tuber) is able to adapt the amplitude of saccades. The
functions of the D1 and D2 and other as yet undefined hemispheral zones are not as well
known. The D1 and D2 zones probably regulate movements of individual digits, and other
regions may regulate visual smooth-pursuit tracking, eye-hand coordination and higher
aspects of motor planning and cognitive function.
For many years, the cerebellum was thought to be involved only in the generation of
movement. This belief was based on the fact that cerebellar projections had been traced
only to motor areas and cerebellar lesions in humans seemed to cause only motor deficits.
Initial suggestions that the cerebellum participates in cognition arose from anatomic
connections that were postulated to exist due to the parallel expansion of the frontal lobe,
lateral cerebellum, and dentate nucleus. Then retrograde transneuronal transport of
special virus strains in monkeys demonstrated many specifics of these connections
(Middleton & Strick, 1998). These transneuronal studies have suggested the following
general principles: 1) cerebral cortical areas that project to the cerebellum (motor,
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premotor, and lateral intraparietal areas, and some of the non-motor areas of the
prefrontal cortex are the target of cerebellar output; 2) cerebral cortical areas that do not
project to the cerebellum are themselves not the target of cerebellar output; and 3) the
cerebellar output channels to different cortical areas are topically distinct zones of the
dentate nucleus (Fig. 32.8).
Fig. 32.8 Output channels to four areas of cerebral cortex.
Anatomical arrangement of separate output channels in the monkey dentate (DN) and
interpositus (IP) nuclei, after virus injections into different cortical areas (M1arm, PMvarm,
area 46, area 9l). Solid dots indicate neurons that were labeled by virus retrogradely
transported from the cortex in three adjacent sections at the antero-posterior location
indicated below each nuclear outline (P7.5, P8.0, P8.5, P9.5). (D, dorsal; M, medial).
(Reproduced from Middleton and Strick, 1998, with permission)
Summary
The organization of the mossy and climbing fiber input to the cerebellum is appropriate
for regulating neuronal populations in other parts of the brain that control movement,
autonomic function, and cognitive operations.
THE NEURONS AND THEIR SIGNALS
The purpose of this section is to relate the cellular properties of cerebellar neurons to
their signal processing operations. The diverse constellation of neurons in the cerebellar
cortex is summarized in Figure 32.9, which is a perspective drawing of a folium that
integrates the transverse and saggital views of the cerebellar cortex given earlier (Fig.
32.2 & 32.3). The 3-D perspective highlights the orthogonal relationship between the
flattened PC dendritic trees and the sheet of parallel fibers providing convergent input.
The drawing also illustrates the arrangement of the three types of inhibitory interneuron
(Golgi, stellate and basket cells) and other factors within this matrix.
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Fig. 32.9 Cells and circuitry of the cerebellar cortex.
This 3-D representation integrates the simplified transverse and sagittal views of a
cerebellar folium shown earlier in Figs. 32.2 and 32.3. (Adapted from Heimer, L., The
Human Brain and Spinal Cord, Springer-Verlag, 1995).
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Purkinje cells shape the spatiotemporal patterns of cerebellar outflow.
The most remarkable neurons of the cerebellum are the Purkinje cells. The innervation of
a PC by an individual climbing fiber is quite exceptional, virtually climbing all over the
proximal dendrites and making multiple excitatory synapses (Fig. 32.10). Except for the
fact that CFs fire at very low rates, this would dominate the discharge of the PC. Instead,
PCs have two characteristic types of discharge that can be observed with either
extracellular or intracellular recording electrodes, namely the repetitive simple spikes that
are mediated by PF input and the occasional complex spikes that are mediated by CF
input (Thach, 1998). Recorded near the cell body under quite stable conditions, complex
spikes appear as high frequency wavelets (Fig. 32.11C2), but they are also recorded as a
large spike followed by a wave that lasts for a few msec.
Fig. 32.10 Climbing Fiber-to-Purkinje Cell Pathway and Synapses.
(A) Climbing Fiber-to-Purkinje Cell Pathway. Varicose branches of a single climbing
fiber (labeled blue with a lectin) cling to the proximal dendritic domain of an individual
Purkinje cell arbor (labeled brown with antiserum to calbindin). (Courtesy of Rossi, F.,
Borsello, T., Vaudano, E., and Strata, P. Neuroscience 53:759-778, 1993). (B) Climbing
fiber-Purkinje cell synapses. The electron micrograph shows a climbing fiber varicosity
(CF) in synaptic contact with spines (arrows) of the proximal dendritic domain of the
Purkine cell arbor (Pd). (Adapted from Larsell O. and Jansen, J. Eds., The Cerebellum.
Minneapolis: Minnesota Universtity Press, 1972).
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The vast majority of the action potentials generated by PCs are the large negativepositive potentials shown in Figure 32.11C1, called simple spikes. Simple spikes are
produced by PF input to the PC. They repeat, with occasional pauses, at relatively high
spontaneous rates. The simple spikes recorded from the intermediate cerebellum in the
awake animal show either bursts or pauses in association with movement, and the
intensities of the responses correlate with the velocity and direction of movement (Ebner,
1998). To set the stage for further discussion of the cellular neurobiology of the
cerebellum, we will present a simplified overview of PC and CN signals in the
intermediate cerebellum, and how they relate to the control of an arm movement.
Fig. 32.11 Spike wave-shapes recorded in the awake monkey.
(A) Examples of fast action potentials attributed to mossy fibers. A1: biphasic potential
with a negative afterwave (glomerular potential). A2: predominantly positive potential.
A3: biphasic potential without a negative afterwave. A4: triphasic potential. (B) Example
of a slow negative-positive potential attributed to a Golgi cell. (C) “Simple” (C1) and
“complex” (C2) spikes recorded from a Purkinje cell. (Adapted from Van Kan, Gibson
and Houk, J. Neurophysiology 69: 74-94, 1993).
Figure 35.12 shows schematically four microscopic modules that regulate the activity of
four motor cortical neurons. Microscopic modules are loops between small clusters of
cortical and CN neurons, a whole array of which comprise the macroscopic module
illustrated in Figure 32.5. Each of the numbered neurons is assumed to command
movement in one of 4 directions – motor cortical neuron 1 commands upward movement,
2 rightward, 3 downward and 4 leftward. Each of the output neurons is reciprocally
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connected (via thalamic and pontine neurons) to a CN neuron that is regulated by
inhibitory input from a PC (actually a parasaggital row of about 300 PCs). Adjacent to
each module are two waveforms, meant to represent the discharge over time of an
associated Purkinje cell (upper trace) and the nuclear neuron (lower trace) to which it
projects. The lower traces also represent the motor cortical neurons linked to the CNs,
since they will be caused to burst simultaneously by the module’s reciprocal corticalcerebellar loop.
Fig. 32.12 Signals and circuits regulating the direction of an arm movement.
The diagram illustrates four motor cortical cells, labeled 1-4, that move the arm upward
(1), rightward (2), downward (3) or leftward (4). Each is reciprocally connected with a
different microzone of the cerebellum, so as to form a microscopic module. The traces
next to each module illustrate how pauses and bursts in Purkinje cell discharge would
regulate the nuclear (and motor cortical) cell’s activity. Note the high spontaneous
discharge of the Purkinje cells (dashed lines reference no discharge).
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Divergence of fibers within the limb premotor network (arrows between modules) allows
activation to spread laterally among adjacent modules. A sensory stimulus (flash of light,
or a tone) might produce just a tiny burst of activity in neuron 1. This small activation,
while insufficient to drive movement, can initiate an amplification process. Since that
loop’s PC inhibition is turned off, activity can reverberate around this loop. Positive
feedback would enhance the intensity and extend the duration of this reverberating
activity, producing a substantial command signal for transmission to an agonist muscle
for upward movement. Activity would also tend to spread to the modules controlling
neurons 2 and 4, since their PCs are only producing moderate inhibition. This would
command a co-contraction of right and left muscles, which would serve to stabilize the
limb. In contrast, activity would not spread to the module controlling neuron 3, since its
PC is bursting and is producing strong inhibition. Therefore muscles that tend to move
the limb downward would be relaxed. In a more realistic model, there would be many
more such microscopic modules, each controlling movements in intermediate directions
that are distributed throughout the workspace.
This example assumes that the PCs have been programmed to discharge with an
appropriate time course. The upward movement command is a strong burst because its
PC paused completely for the duration of the movement command. There is no
downward movement command because its PC fired a strong burst during the period
when positive feedback was present in other loops of the limb premotor attractor
network. The rightward and leftward movement commands are intermediate in intensity,
because their PCs do not stray much from their spontaneous level of activity. While these
assumed patterns of PC activity are compatible with current neurophysiological data, the
field is still lacking definitive experiments showing that the correct PCs in the cerebellar
cortex generate the most appropriate patterns. The motor learning mechanisms discussed
at several points in this chapter should be capable of insuring this, but the experimental
evidence remains incomplete.
There is also increasing evidence that the cerebellum, motor cortex and red nucleus are
organized not in terms of preferred directions of hand movement, but rather in terms of
functionally useful groups of muscles (Miller et al. 2002). It is easy to see how such a
system of preferred muscle synergies could be controlled by interconnected groups of
cortical-cerebellar processing modules such as the ones discussed above. Arrays of
modules controlling grasp muscles might be adjacent to, and partially interconnected
with, modules controlling limb extension muscles. Postural responses could be
coordinated by similar interconnections with modules controlling muscles of the neck,
trunk and legs.
Granule, Golgi and brush cells process the excitatory mossy fiber input.
The MF signals that convey state information to the cerebellum excite granule cells
within giant synaptic structures called glomeruli. The expanded drawing of a glomerulus
in Figure 32.9 shows that its core ingredient is a large expansion of the mossy fiber,
which occurs along its branches or at the terminals. The glomerulus is packed with
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synaptic vesicles, and with mitochondria that fuel the manufacturing of the vesicles. The
dendrites of nearby granule cells send claw-like protrusions into the glomerulus where
they form multiple small synaptic junctions (Fig. 32.13). Because of the large size and
glial surround of the glomerulus, extracellular electrodes are able to record the signals
transmitted by MFs, in one of several ways illustrated in Figure 32.11A. The full fledged
glomerular potential (A1) has a biphasic presynaptic component, produced when the
action potential invades the glomerulus, followed by a slower negative wave, produced
when excitatory postsynaptic current flows through the numerous excitatory synaptic
junctions. When the negative wave is missing, the presynaptic component takes on one of
the three other configurations shown in Figure 32.11A2, A3 & A4. MFs in the
intermediate cerebellum discharge at frequencies that are graded over a broad range, and
different fibers signal a variety of sensory and efference copy information.
Fig. 32.13 Mossy fiber-to-granule cell synapses in a cerebellar glomerulus.
The electron micrograph shows the central mossy fiber terminal (MF) forming
asymmetric synaptic junctions (circled) with surrounding granule cell dendrites. The
granule cell dendrites form symmetric synaptic junctions (boxed) with terminals of the
Golgi cell axon (Ga), which are labeled by immunogold particles (small solid dots) using
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antiserum to GABA. Arrow indicates astrocytic lamellar processes forming the peripheral
glial sheath. Asterisk marks the shaft of a granule cell dendrite entering the glomerulus.
(Adapted from Heimer, L., The Human Brain and Spinal Cord, Springer-Verlag, 1995).
While MF activation of a glomerulus can be recorded in awake behaving animals with
extracellular microelectrodes, our knowledge of synaptic integration by the granule cell
depends mostly on intracellular recordings from brain slices (Hansel et al. 2001), due to
the fact that the small extracellular spikes produced by their tiny axons are obscured by
electrical noise. MF input activates both AMPA and NMDA receptors and, due to the
latter, exhibits excellent temporal summation. Excitatory transmission is moderated by
the GABAergic inhibition sent to the glomerulus by Golgi cells. The latter neurons
produce slower and larger extracellular action potentials than MFs (Fig. 32.11B). Their
dendrites branch broadly, mainly in the molecular layer, and they discharge at relatively
steady rates that reflect the overall level of PF activity in the overlying molecular layer.
The computational ideas originated by Marr and Albus in the 70’s appear to be
reasonably valid (Houk et al. 1996). Golgi-cell inhibition appears to function like an
automatic gain control, normalizing the amount of PF input, so as not to overwhelm PCs,
but at the same time allowing the PF state vector to express many diverse patterns which
can then be selectively detected by individual PCs. Because the granule cells receive
input from about 4 different MFs, the MF-granule cell system should create an expanded
representation of state that is kept sparse by Golgi inhibition.
Unipolar brush cells (Fig. 32.9) are found in the granular layer of the vermal and
intermediate zones and the vestibulocerebellum (Nunzi et al. 2001). They are strongly
excited by individual MF inputs, or by other brush cells, and they strongly excite nearby
granule cells. This circuit serves to amplify the intensity and duration of MF input. This
is probably important for the enhancement and short-term storage of state information
about the orientation of the organism that is characteristic of the vestibulocerebellum.
Climbing fibers transmit training information via the inferior olive.
The CF pathway originates in the inferior olive of the brainstem. These cells display
electrical activity analogous to that present in the heart -- action potentials with long
plateaus followed by long refractory periods -- causing CFs to fire at very low rates
(irregular at about 1/sec). Many olivary neurons detect sensory events, but are inhibited
by GABAergic inputs from the CN. This combined excitatory and inhibitory input helps
to signal the occurrences of errors. When the same sensory event occurs in a context that
does not signify error, the small CN neurons can inhibit their responses. Olivary cells are
electrotonically coupled to each other and show a slight tendency to oscillate at
approximately 10 Hz (Welsh et al. 1995). The diversity of the receptive fields of olivary
neurons insures a relatively private training signal that is then transmitted to parasaggital
rows of about 10 PCs. The best current examples of error detection are the CFs that
project to PCs in the flocculonodular lobe. They signal the slip of visual information
across the retina, which is indicative of an improperly regulated eye movement command
(Simpson et al. 1996).
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Non-laminar afferents bring neuromodulatory influences.
In addition to MFs and CFs, the cerebellum receives several types of afferents that have
non-laminar distributions of their terminals. These non-laminar afferents orginate from
neurons in the locus ceruleus, the raphe nuclei, or from widely distributed choline
acetyltransferase(ChAT)-positive brainstem neurons, and from the hypothalamus. These
afferents innervate the cerebellar nuclei and all layers of the cerebellar cortex, with some
preference for the molecular layer. Afferent fibers from the locus ceruleus arrive via the
superior cerebellar peduncle and release norepinephrine in the cerebellar cortex, fibers
from the raphe nuclei release serotonin (5HT), fibers from ChAT-positive neurons release
acetylcholine (ACh), and fibers from the hypothalamus are in part histaminergic. The
non-laminar fiber systems modulate the excitability of PCs and other cerebellar neurons.
Non-laminar afferents and their synapses are best identified with the help of cytochemical
markers and tract tracing molecules. These afferents have active zones and postsynaptic
densities, although it is likely that they may also release their transmitters at nonspecialized regions of their terminal branches.
Molecular layer interneurons dampen Purkinje cell excitability.
The stellate and basket cells in the molecular layer inhibit PCs via GABAergic synapses.
Stellate cells, which are scattered throughout the molecular layer, provide a moderating
influence that dampens large fluctuations of excitatory PF input. The dendrites of basket
cells are longitudinally oriented and their axonal trees innervate parasaggital rows of PCs,
forming basket-like terminations that surround the PC bodies and form paint brush
extensions around their initial axon segments. Since PCs have high spontaneous
discharge rates, the basket cell’s specializations seem appropriate for initiating the pauses
that punctuate their spontaneous activity. The extracellular potentials of basket cells have
not yet been definitively identified in awake animals, but the bi-phasic potentials
recorded just above the PC layer are appropriate candidates. These units show bursts and
pauses analogous to those recorded from PCs. Basket and stellate cells have dendrites
carrying a low density of spines and receive most of the excitatory synapses on their cell
bodies and dendritic shafts from both PFs and collaterals of CFs. These contacts are
intermixed with inhibitory synapses from other basket and stellate cells. Basket cells are
also inhibited by recurrent collaterals of PC axons.
Purkinje cells have special computational features.
The ionic currents that influence PC discharge are numerous. However, the calcium Pcurrents underlying plateau potentials in PC dendrites (Llinas & Sugimori, 1980) are
especially important from two functional perspectives: (1) promoted by excitatory input
from PFs, P-current mediated plateau potentials are responsible for the relatively high
spontaneous firing rates (≈50 imp/s) of PCs and (2) the influx of calcium resulting from
these currents is one of the factors that contributes to the motor learning mediated by the
synaptic plasticity of PFÆPC synapses, as will be elaborated in the following section.
PC dendrites are forced, by the balance between their excitatory and inhibitory synaptic
input, to make transitions in their internal state – transitions back and forth between a
hyperpolarized state of low excitability and a depolarized plateau state of high
excitability (Houk et al. 1996). Inhibitory synaptic input from molecular layer
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interneurons can initiate transitions from the depolarized to the hyperpolarized state. Such
transitions are important since they generate the pauses in PC discharge that remove the
spontaneous inhibition of CN neurons that exists spontaneously in the cerebellum. This
permits the CNs that they target to fire at very high frequencies (100-600 imp/s). This
succession of events accounts for the buildup of intense activity in the reciprocal loop of
the module 1 illustrated in Figure 32.12. The high CN firing rate amplifies the agonist
movement command that was initiated in the motor cortex by, for example, a sensory
cue.
A little later, after the movement is underway, we speculate that module 1’s PC detects
the occurrence of a critical pattern in its PF state vector, signifying that the moment has
arrived to terminate the movement command. Then, after the conduction delays in the
neuromuscular system, the movement can come to a graceful termination, hopefully at
the desired endpoint. If the corresponding PFÆPC synapses have learned to recognize
this truly critical state, the PC dendrite will receive appropriately strong excitatory input
at the critical moment, thus promoting the transition back to the plateau state of dendritic
depolarization, which causes the PC to resume its moderately high spontaneous firing
rate. The resumption of potent inhibitory input to the CN neuron turns off its intense
firing, thus terminating the module’s movement command.
A different succession of events may account for the suppression of motor commands to
antagonist motor neurons. PCs that regulate module 3 in Figure 32.12 are shown to
substantially increase their discharge at about the same time that the agonist-connected
PC in module 1 pauses. The antagonist-connected PC is assumed to have detected the
occurrence of a pattern in its state vector calling for the initiation of a movement opposite
to the one it promotes if it pauses. Instead, it bursts, which helps to suppress the
generation of an antagonist command. Presumably some of its dendrites were sitting in
their hyperpolarized states, and some of the corresponding PFÆPC synapses detected the
initiation of movement commands in the movement’s agonist muscles. This promotes
transitions to depolarized states, which promotes intense firing of that PC. The intense
firing inhibits the CN to which the PC projects, preventing it from amplifying discharge
in the output neuron(s) that it targets. This helps to suppress movement commands to the
antagonist motor neurons.
Modules 2 and 4 in Figure 32.12 are regulated in a less intense fashion. Their PCs exhibit
a modest increase in firing at movement onset, which slightly increases the inhibition sent
to their CNs. This tends to dampen the buildup of positive feedback in their reciprocal
loops, while not entirely inhibiting it. This is because the overall activity of the limb
premotor network is strongly enhanced, which brings an excitatory influence to those
loops.
By tracing through the logic of this simplified example, one can begin to appreciate the
critical role played by the large array of Purkinje cells in the intermediate cerebellar
cortex. The spatiotemporal pattern in this PC array plays a critical role in regulating the
buildup of positive feedback in the limb premotor network, shaping it into a composite
movement command that moves the limb toward an object that the organism wants to
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manipulate. Although all of this PC array’s activity is important, a particularly critical
feature is the detection of PF states indicating that the time has come to terminate the
composite movement command. If this didn’t happen, the resultant movement would be
hypermetric. In fact, the immediate effect of lesions confined to the cerebellar cortex is
hypermetria. However, over the course of functional recovery, other circuitry in the brain
(for example, intracortical circuitry and/or the loop through the basal ganglia shown in
Figure 32.5) evidently adapts in a manner that suppresses the buildup of positive
feedback in the limb premotor network.
Long-term depression mediates motor learning.
The strategic role of PFÆPC plasticity in motor learning has been mentioned several
times earlier in this chapter. Long-term depression (LTD) is the name given to the
synaptic plasticity of PFÆPC synapses (Ito, 1984; Fig. 32.14). While other types of
plasticity have also been found in the cerebellum (Hansel et al. 2001), LTD is particularly
important. Cerebellar LTD appears to differ from the long-term potentiation (LTP)
present in cortical neurons (Chapter 50) in several significant respects: (i) it desensitizes
postsynaptic receptors instead of sensitizing them (depression instead of potentiation), (ii)
it uses a different set of second messengers, (iii) it appears to be a 3-factor learning rule
instead of the predominantly 2-factor Hebbian rule associated with LTP at most other
sites in the CNS (Houk & Alford, 1996). The two factors in the Hebbian rule are activity
of a particular synapse and activity of the postsynaptic neuron. The three factors
associated with cerebellar LTD are discussed below, and Figure 32.15 summarizes some
of the salient steps in this synaptic modification process.
Fig. 32.14 A dendritic spine of a Purkinje cell.
Electron micrograph of a dendritic spine arising from a spiny branchlet of the Purkinje
cell arbor. The spine forms an asymmetric synapse with a parallel fiber varicosity. Actin
forms a lattice in the spine head and parallel microfilaments in the spine neck. Dense
spots on the membrane of the endoplasmic reticulum of the spine represent the large
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cytoplasmic domains of InsP3 receptors. These function as Ca2+ channels and are
extremely abundant in Purkinje cells. Side branches of the astrocytic Bergmann glia
fibers (asterisks) surround the synaptic profiles, with the exception of the synaptic
apposition.
Fig. 32.15 Multi-level principles appropriate for driving motor learning in the
cerebellum.
The red arrows mark three important factors in the learning rule. At the level of an
individual spine, the glutamate released by a PF causes both a depolarizing current,
mediated by AMPA receptors, and a metabotropic activation, mediated by mGluR1
receptors. The latter is a spine-specific factor. The depolarizing currents produced by
several spines along the dendrite may summate sufficiently to produce a plateau
potential. This factor signifies the PC dendrite actively participated in a movement.
Metabotropic activation of individual spines combines with dendritic depolarization to
activate PKC, which then phosphorylates the AMPA receptor to produce a trace of prior
coincident synaptic and dendritic activity. Meanwhile, many dendrites and many PCs
regulate the composite movement command that, after some time delay, produces a
movement. If an error is then detected, the CF fires and that can activate PKG to
consolidate any trace of LTD that is present in the spine.
Factor 1. When a PF releases glutamate at a particular synaptic spine, like the one
illustrated in Figure 32.14 & 32.15, it activates two types of glutamatergic receptor -AMPA and mGluR1. Activation of the spine’s AMPA receptors opens channels that
permit depolarizing currents to flow into the spine and out into the dendrite, influencing
the postsynaptic depolarization of that dendrite, and the activity of the entire PC. In
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combination with the currents produced by many other activated spines, this synaptic
activation contributes to the internal state of the dendrite and may initiate a plateau
potential. If so, the depolarization of the dendrite spreads back into the spine (factor 1,
shown by a red arrow in Fig. 32.15) to augment spine depolarization. The activity of all
of the PC’s dendrites combines to control PC discharge.
Factor 2. In contrast, activation of the spine’s mGluR1 receptors initiates chemical
changes that are confined to that particular synaptic spine, as summarized in Figure
32.15. The localization of factor 2 to one spine (shown by another red arrow in Fig.
32.15) insures that LTD will be synapse specific (Wang et al. 2000). Only the synaptic
weight of this particular PFÆPC synapse is made eligible for modification by the
learning rule. If the synapse is excited at nearly the same time that the dendrite is in its
plateau state, factors 1 and 2 synergize. Through second messenger pathways, there is a
local increase in the level of calcium in the spine. Then, through relatively slow second
messenger pathways, this phosphorylates the spine’s AMPA receptors causing them to
become desensitized. To summarize this from a computational standpoint, there is a
nearly immediate activation of the dendrite and the PC, and a slow phosphorylation of the
spine’s AMPA receptors. The slowness of the latter process provides a biological basis
for a slow rise and decay of an eligibility trace signifying that this synapse is eligible for
LTD (Barto et al. 1999). It became eligible because it was just active, and because the
dendrite participated (though slightly) in helping to terminate the movement command
that this PC helps to regulate.
Factor 3. Meanwhile, a composite motor command is being formulated by the regulatory
actions of the array of PCs that shape activity in thousands of the microscopic modules
analogous to the ones exemplified in Figure 32.12. The thousands of elemental
movement commands, transmitted by thousands of neurons comprising the output
population, function in grand combination to collectively control the actual movement
that is eventually made. Only then can this action be evaluated by its sensory
consequences, so as to provide a training signal transmitted by CFs (third red arrow in
Fig. 32.15). The corresponding CFs are presumed to detect cases in which there are errors
in the endpoint of the movement. Thus, after a time delay of up to a few hundred
milliseconds, a particular CF either fires or remains silent. Its firing signifies that a faulty
action is being produced, and an adjustment in synaptic efficacy is needed to make the
error less likely in the future. The precise mechanism is still being investigated, but there
is evidence that CF firing leads to an activation of protein kinase G (PKG in Fig. 32.15).
Activated PKG prevents the dephosphorylation of recently phosphorylated AMPA
receptors. Thus, if the eligibility trace mentioned in the previous paragraph has not yet
decayed, this CF discharge could consolidate the desensitization of the AMPA receptors,
resulting in a sustained decrement in synaptic weight, which is the definition of LTD.
All three factors need to be satisfied to produce a computationally appropriate learning
rule (Houk & Alford, 1996). Factor 1 (a postsynaptic factor) signifies that the dendrite
and PC actively participated in the impending action. Factor 2 (a synapse-specific factor)
signifies that this particular PFÆPC synapse helped the PC to participate in the action.
Factor 3 (a training signal) signifies that the action in which the synapse and PC
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participated resulted, after a substantial time delay, in an error. The slow eligibility trace
helps to compensate for the time delay.
We have only considered processes that depress synaptic efficacy. Learning rules need to
work in both directions to train a network effectively. There is evidence for a reversal of
LTD when CFs fail to detect errors, but the mechanisms have yet to receive the attention
that they deserve. Another important topic is the morphological consolidation of PFÆPC
synaptic plasticity that occurs with long-term training (Kleim et al. 1998).
Cerebellar nuclear cells are of two types.
The cells of the cerebellar nuclei consist of large excitatory neurons (glutamatergic) and
small inhibitory neurons (GABAergic and/or glycinergic). The large nuclear neurons are
induced to fire at high frequencies, apparently when activated by MF collaterals, and
their firing frequency is reduced when PCs burst intensely (Miller et al. 2002). The
nuclear cells also exhibit some spontaneous firing under isolated conditions, which
results from a tonic cation current mediated mainly by sodium influx (Raman 2000). Less
is known about the electrophysiology of the small nuclear cells. However, their discharge
should inhibit the olivary neurons to which they project, and this mechanism appears to
cancel out sensory responses during certain phases of behavior, which probable serves to
refine the training signals transmitted by CFs.
Classical conditioning depends on the cerebellum.
One of the first forms of learning to be analyzed neurobiologically is the classically
conditioned reflex (Thompson, 1986). It was discovered that the intermediate cerebellum
is crucial for expression of the conditioned eyeblink reflex (Chapter 51). To comprehend
how these findings relate to the neurophysiology of the cerebellum, it is helpful to relate
the modular concept of cerebellar signal processing (Fig. 32.4) to the neural circuitry that
is believed to mediate the conditioned eyeblink movement (Houk et al. 1996; Raymond et
al. 1996). Conditioned eyeblinks appear to involve the intermediate cerebellum, parts of
it that generate the motor commands that are sent to brainstem networks that control
eyelid muscles. When the associated circuitry is labeled with an activity-dependent
marker, one can visualize two separate networks (Keifer et al. 1995). One links the
intermediate cerebellum with the red nucleus -- it is required for well coordinated
conditioned reflex responses but not for the basic unconditioned reflex. The latter is
controlled by a brainstem network that is required for both conditioned and
unconditioned responses.
Plasticity in the cerebellum is probably only responsible for adjusting the metrics of the
motor responses (Welsh & Harvey, 1989), and not for making the associative link
between the conditioned and unconditioned stimulus. The conditioned stimulus functions
as a sensory cue (one of the Diverse Inputs in Figure 32.4) for initiating a transition to the
active state of the rubrocerebellar attractor network (Fig. 32.5), so the acquisition of a
new cue is more likely to be regulated by pathways that pass through the basal ganglia.
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Summary
The neurons of the cerebellum have diverse anatomic and physiologic specializations that
appear to facilitate: (1) the creation of input diversity in arrays of parallel fibers, (2) the
detection by Purkinje cells of salient patterns that are present in these arrays, and (3) the
use of these detection outcomes to regulate the temporal pattern of activity in the
cerebellar nuclear neurons to which they project. Purkinje cells learn to do this through a
unique form of synaptic plasticity that couples efficacy to regulatory performance, as
monitored by climbing fibers.
ACTIVATION AND INACTIVATION STUDIES
We can also learn about the functions of the cerebellum by studying what happens when
parts of it are activated or inactivated. Activation of the cerebellum results naturally when
we use our brain networks in the course of appropriately complex behaviors, and
particular regions of the cerebellum can be artificially activated by electrical stimulation.
Inactivation of the cerebellum can be produced with lesions, which cause permanent
changes, or with reversible inactivations produced by microinjections of pharmacological
agents. This section summarizes what we have learned about cerebellar function from
activation and inactivation studies.
Activation studies reveal diverse functions.
For many years, the cerebellum was thought to be involved only in the execution of
movement, but this appears to be wrong. Human brain imaging studies indicate that the
cerebellum also participates the planning of complex movements and in a variety of
cognitive and problem solving functions (Frackowiak et al. 1997). For example, the
cerebellum is activated when subjects make sequential movements, and even when they
imagine or passively observe movement without making movements themselves.
Furthermore, the lateral cerebellum is activated in a language task, in which subjects are
asked to generate appropriate verbs for visually presented nouns. The observed activity is
over and above the activation that occurs when nouns are simply read, indicating that the
cerebellum somehow contributes to the generation of appropriate verbs. Interestingly,
once a verb generation task has been rehearsed, cerebellar activation diminishes (van
Mier et al. 1998). As an additional example, the ventral dentate is activated bilaterally
when subjects work to solve a difficult pegboard puzzle, and this activation is three to
four times greater in magnitude than during simple peg movements (Kim et al. 1994).
Overall, these studies indicate that the newer parts of the cerebellum, the hemispheres
and the dentate nucleus, are activated in ways well beyond those required for the
execution of movements.
Behavioral observations of humans with cerebellar damage supports the conclusion that
the cerebellum participates in non-motor aspects of cognition. In one case study, a
cerebellar patient showed impaired performance on the verb generation task described
earlier; he was unable to detect errors in his performance of the task and did not exhibit
normal practice-related learning. Interestingly, this patient performed normally on
standard intelligence and memory tests. In another study, cerebellar patients had deficits
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in both the production and the perception of a timing task. Lateral cerebellar regions
contribute to the perception of time intervals, while medial cerebellar regions help to
mediate the timing of implemented responses.
An older method that was used to activate the cerebellum is electrical stimulation. In the
absence of anesthesia, either contractions or relaxations of muscles, and resultant
movements, can be elicited by electrical stimulation. The movements affected are
diverse, and they depend, as expected, on the zone of the cerebellum that is stimulated.
Sometimes the movements elicited are relatively complex sequences, consistent with the
complex motor planning functions mentioned earlier.
Humans with cerebellar damage exhibit motor deficits.
In the early 1900s, Gordon Holmes described the movement deficits associated with
discrete cerebellar lesions in humans that were caused by gunshot wounds. His
descriptions provide the basis for classifying clinical cerebellar syndromes according to
seven basic deficits.
1. Ataxia is a condition that involves lack of coordination between movements of body
parts. The term is often used in reference to gait or movement of a specific body part, as
in ataxic arm movements.
2. Dysmetria is an inability to make a movement of the appropriate distance or direction.
Hypometria is undershooting a target, and hypermetria is overshooting a target.
Patients with cerebellar damage tend to make hypermetric movements when they move
rapidly and hypometric movements when they move more slowly and wish to be
accurate.
3. Dysdiadochokinesia is an inability to make rapid, alternating movements of a limb. It
appears to reflect abnormal agonist-antagonist control.
4. Asynergia is an inability to combine the movements of individual limb segments into
a coordinated, multisegmental movement.
5. Hypotonia is an abnormally decreased muscle tone. It is manifest as a decreased
resistance to passive movement, so that a limb swings freely upon external perturbation.
Often, hypotonia is not present in cerebellar patients or is present only during the acute
phase of cerebellar disease.
6. Nystagmus is an involuntary and rhythmic eye movement that usually consists of a
slow drift and a fast resetting phase. After unilateral cerebellar lesion, the fast phase of
nystagmus is toward the side of the lesion.
7. Action tremor, or intention tremor, is an involuntary oscillation that occurs during
limb movement and disappears when the limb is at rest. Cerebellar action tremor is
generally at a low frequency (3-5 Hz). Titubation is a tremor of the entire trunk during
stance and gait.
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Cerebellar damage also causes deficits in motor learning. Studies of prism adaptation
have shown that some patients with cerebellar damage are unable to adapt their hand-eye
coordination to the visual displacement produced by the prism (Fig. 32.16). Most of these
symptoms can be explained as a basic problem of learning to control the direction and
amplitude of a movement (dysmetria), making it necessary use multiple corrective
submovements (action tremor).
Fig. 32.16 Prism adaptation test.
(A) Eye-hand positions after adaptation to base-right prisms in a control subject. The
optic path is bent to the subject's right, giving a larger view of right side of her face. Her
gaze is shifted left along the bent light path to foveate the target in front of her. Her hand
position is ready for a throw at the target in front of her. (B) Horizontal locations of throw
hits displayed sequentially by trial number. Deviations to the left are negative values;
deviations to the right, positive. While the subject is wearing the prisms (gaze shifted to
the left), the first hit is displaced 60 cm left of center. Thereafter, hits tend toward 0.
After the prisms are removed, the first hit is 50 cm right of center. Thereafter, hits tend
toward 0. Data during and after prism use have been fitted with exponential curves. The
decay constant is a measure of the rate of adaptation. The standard deviation of the last
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eight preprism throws is a measure of performance. Gaze and throw directions are
schematized with arrows. Inferred gaze (eye and head) direction assumes the subject is
foveating the target. Roman numerals beneath the arrows indicate times during the prism
adaptation experiment (see B). (C) Failure of adaptation in a patient with bilateral
infarctions in the territory of the posterior inferior cerebellar artery. (Adapted from
Martin, T. A., Keating, J. G., Goodkin, H. P., Bastian, A. J. and Thach, W. T. (1996).
“Throwing while looking through prisms. I. Focal olivocerebellar lesions impair
adaptation.” Brain 119: 1183-1198)
Reproduced from Bastian, Mugnaini and Thach, in Zigmond M. J. et al. Eds.,
Fundamental Neuroscience. S. Diego: Academic Press, 1996)
Localized inactivations produce modular deficits.
Damage to the cerebellar nuclei causes unique behavioral deficits (Thach, 1998).
Ablations of the fastigial nucleus in cat and monkey dramatically impair movements
requiring control of equilibrium, such as unsupported sitting, stance, and gait.
Longitudinal splitting of the cerebellum along the midline also produces very significant
and long-lasting disturbances of equilibrium. In humans, lesions in the anterior vermus
preferentially impair movements requiring equilibrium control (Timmann & Horak,
1995). Much of the fastigial nucleus seems to be preferentially involved in movements
like stance and gait.
Ablations of the interpositus nucleus in monkeys cause action tremor as the animals reach
for food. Temporary inactivation of the interpositus nucleus and adjacent regions of
dentate with cooling probes elicits tremor that is dependent on proprioceptive feedback
but is uninfluenced by vision. Focal pharmacological inactivations within the
intermediate region disrupt the use of particular synergies related to hand use and limb
positioning (Mason et al. 1998). Damage to the cortex and the inferior olive prevents
many kinds of motor adaptation, including the acquisition of new and novel muscle
synergies. These studies support the ideas reviewed earlier in this chapter, namely that
the intermediate cerebellum regulates the composite motor commands that control the
metrics of coordinated upper and lower extremity limb movements. Damage to the
posterior vermis and floculonodular lobe produce analogous disorders of eye movements
(Chapter 33).
Summary
Activation and inactivation studies of the cerebellum are consistent with the concepts
promoted earlier in this chapter. The cerebellum learns how to regulate neuronal
populations in different parts of the brain that control different kinds of movement,
autonomic function, and cognitive signal processing.
PHYLOGENETIC AND ONTOGENETIC DEVELOPMENT
There are two kinds of development – phylogenetic, in the course of evolution, and
ontogenetic, from the embryo to the adult animal. Both involve an enlargement of the
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cerebellum that parallels the enlargement of other parts of the brain, but there are many
exceptions to the concept that ontogeny recapitulates phylogeny.
The cerebellum reflects the course of vertebrate evolution.
The cerebellum is present in all vertebrates from the primitive myxinoids up through the
advanced primates, although its parts show considerable species variation. In the lamprey
(agnathans), the cerebellum is a rudimentary structure that assists the functions of the
well-developed vestibuloocular, vestibulospinal, and reticulospinal systems, and is
equated to the flocculonodular lobe. The cerebellum is much larger in fishes, where a
corpus cerebelli distinctly appears. In electric fish (Box 32.3), the cerebellum is
extraordinarily developed and includes lobes not present in other vertebrates. The
cerebellum increases further in reptiles and birds, although it consists nearly exclusively
of the vermal zone of the corpus cerebelli and the flocculonodular lobe. Well developed
glomeruli are present in reptiles and birds. Basket cells, however, are absent in reptiles
and are well developed in birds, pointing to evolutionary sophistication of inhibitory
interneural connections. In birds, the vermis is distinctly foliated and lobules I through X
are clearly apparent, but the basilar pontine nuclei are rudimentary and project to small,
flattened lateral zones. In mammals, the lateral cerebellar zone expands to form the
cerebellar hemispheres, in register with the development of the pontine nuclei and the
cerebral cortex.
Box 32.3: THE GIGANTOCEREBELLUM OF MORMYRID FISH
Weakly electric fish of the family Mormyridae have an enormous cerebellum that covers
their brain like our cerebral cortex covers our brain. This gigantocerebellum explains why
these fish have a large brain to body ratio of 0.03 and why their brain uses 60% of the
oxygen that they take in. For comparison, our brain to body ratio is only 0.02 and our
brain uses only 20% of the oxygen we take in. The gigantocerebellum is composed of a
ribbon of Purkinje cells, 0.3mm high and 1.0 meter long, that is folded back on itself
repeatedly to fit within the skull. Different regions of the cerebellum receive
electrosensory, auditory and lateral line input from midbrain structures homologous to the
mammalian inferior colliculus. These cerebellar regions project back to the same
midbrain sensory structures from which they receive their input. Thus, this cerebellum is
more involved in processing sensory input than in generating motor output. Although the
type of processing that is done by the fish’s cerebellum is still unknown, cerebellum-like
structures in another part of these fish brains, the electrosensory lobe and the dorsal
octaval nucleus, generate memory-like expectations of sensory input by means of plastic
changes at synapses between parallel fibers and Purkinje-like cells (Bell, 2001),
analogous to what probably occurs in the cerebellum itself.
Curtis C. Bell
Bell, C.C. Memory based expectations in electrosensory systems. Current Opinion in
Neurobiology., 11, 481-487, 2001.
In evolution, the size of the cerebellar cortex increases more distinctly than that of the
cerebellar nuclei, reflecting a greater emphasis on the computational aspects of
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information processing. While lobulation, in principle, is fairly consistent across
mammals despite great variation in hemispheric development, foliation shows great
interspecies variation and also substantial intraspecies differences. For example, different
mouse strains may show different folial patterns. In aquatic mammals, the vermal and the
intermediate zones and the underlying fastigial and posterior interpositus nuclei are large,
and the lateral zones relatively small. The vermal and hemispheral portions of the
posterior lobe that receive pontine afferents appear mostly expanded in the primate, and
especially the human. The cortico-ponto-cerebellar input loops back to many areas of the
cerebral cortex. In human, the fibers of the middle cerebellar peduncle vastly outnumber
all of the other connections.
In the embryo, outputs develop first.
Cerebellar neurons develop at different times from two different germinative matrices,
the ventricular epithelium of the cerebellar anlage and the cells of the rhombic lip (Fig.
32.17). The cerebellar anlage, or primordium, begins as bilateral elevations of the dorsal
aspect of the primitive hindbrain and caudal midbrain that grow towards the midline and
ultimately fuse (Liu & Joyner, 2001). The ventricular epithelium of this anlage gives rise
to all cerebellar neurons, except the granule cells, by a process of outward directed
migration. The rhombic lip is an elevation of the rostral hindbrain that extends from the
first to the eighth rhombomere. It gives rise to the external granular layer precursors,
from which granule cells originate by inward migration. The first cells to be formed in
the ventricular zone of the cerebellar anlage are the neurons of the cerebellar nuclei. They
are followed soon after by Purkinje cells, which migrate past the developing cerebellar
nuclei to their ultimate location in the cortex. Precursors committed to differentiate into
unipolar brush cells, Golgi cells, basket cells, stellate cells, and glial cells, which are
produced in later waves, migrate to their final positions after the migration of the
Purkinje cells, and continue to proliferate on their way to the cortex. At an early stage,
small cells migrate from the rhombic lip over the entire external surface of the cerebellum
forming the so-called “external granular layer”, where they remain quiescent till much
later in development.
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Fig. 32.17 Early development of mouse cerebellum.
(A) At embryonic day E9-E10, the rhombic lip (blue and orange) is a zone of
proliferation at the level of the fourth ventricle. Cells in the blue region give rise to the
cerebellar granule neurons and pontine nuclei, whereas cells in the orange region give
rise to other rhombic lip derivatives such as the inferior olivary nucleus. (B) At E13, cells
from the rhombic lip migrate outwards to cover the cerebellar primordium forming the
extrenal granular layer. (C) At early postnatal stages (P1 and further), these committed
granule cell precursors continue to proliferate in the outer external granular layer (EGL),
then become postmitotic and form the inner EGL, and finally migrate from the inner EGL
past the Purkinje cells and into the inner granular layer, where they differentiate into
granule cells establishing synaptic connections with mossy fiber terminals. (Reproduced
from V.Y. Wang and H.Y. Zoghbi, Nature Reviews, 2:484-491, 2001)
Cerebellar input structures develop next.
After the Purkinje have reached the cortical plate, climbing fibers enter the cerebellum
from the inferior olive and begin to innervate the Purkinje cells (Fig. 32.18), each
Purkinje cell receiving input from several climbing fibers. Much later, after the Purkinje
cells have begun to receive synapses from parallel fibers, most of the climbing fiber
contacts with Purkinje cells will be eliminated leaving a private line of one climbing fiber
per Purkinje cell. Mossy fibers also enter the cerebellum and grow to the level just below
the Purkinje cell layer. They will ultimately synapse on granule cells, which have yet to
arrive.
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Fig. 32.18 Development of the Climbing Fiber-Purkinje Cell Pathway.
After the Purkinje cell becomes synaptically competent at postnatal day 5 (P5), branches
from two or more climbing fibers establish contact with short processes arising from the
soma, and successively grow to occupy first the apical dendritic stem (P10) and then the
entire proximal compartment of the dendritic tree (P15). During formation of the spiny
branchlets and parallel fiber synapses, supranumerary climbing fibers branches retract,
leaving only one climbing fiber branch per Purkinje cell (P20). (based on F. Crepel, J.R.
Dupont and R. Gardette In: Gene Expression and Cell-Cell Interactions in the
Developing Nervous System. J.M. Lauder and P.G. Nelson, Plenum Publishing Corp.:
99-113, 1984)
Shortly before and after birth, cells in the external granular layer form two contiguous
strata over the developing cerebellum, the proliferative outer external granular layer, and
the postmitotic, premigratory inner external granular layer (Fig. 32.17). Cells of the inner
layer emit axons at opposite poles that run in the coronal plane (the future parallel fibers)
and then form a third process that extends towards the underlying Purkinje cells utilizing
the radial Bergmann fibers as a scaffold. The third process extends progressively past the
Purkinje cell layer, and the cell nucleus translocates into the process and reaches the
prospective granular layer, leaving the elongating parallel fiber in place (Fig. 32.19).
Normally, all external granules abandon the external granular layer. After this surface-todepth migratory process the differentiating granule cell emits short processes, or
protodendrites, that search the developing mossy fiber terminals to establish connections.
Successively, some of the protodendrites are pruned, while 3-5 of them progressively
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mature into adult granule cell dendrites. At the same time, the mossy fiber terminals
enlarge to accommodate the optimal number of dendritic claws. Concomitantly, the Golgi
cell axons establish their synapses at the base of the claws. Ensheathing of the glomeruli
by lamellar processes of astrocytes becomes progressively more complete. In rodents,
this process of glomerular development takes about six weeks.
It is interesting that the "motor" side of the cerebellar circuit (the deep nuclear cells and
Purkinje cells) forms first, the "sensory" side (MFs and CFs) then arrives in place, and
the "matrix" that connects the two (the granule cells and intrinsic inhibitory neurons) is
the last to develop.
BOX 32.4: GENES CONTROLLING CEREBELLAR DEVELOPMENT
Work on mouse mutant strains with cerebellar abnormalities (eg., weaver, reeler,
staggerer, rostral cerebellar malformation) and genetic studies on formation of the
hindbrain have uncovered a multitude of genes and signaling pathways that govern
development of the brainstem and cerebellum (Wang & Zoghbi, 2001). Moreover, classes
of cerebellar neurons, and especially the Purkinje and granule cells have been shown to
interact during cerebellar development in regulating cell number and
compartmentalization.
The complex interplay of several patterning genes (primarily Otx2, Gbx2, and Fgf8) sets
up the isthmus organizer. This region of the early neural tube, situated at the junction
between mesencephalon and metencephalon, regulates the rostrocaudal patterning of the
midbrain-hindbrain and the formation of the cerebellar primordium. Other sets of
intracellular and secreted gene products control discrete steps in cerebellar development.
Math1 and genes coding numerous zinc finger proteins play major roles in generation,
proliferation and movement of granule cell precursors in the germinal matrix of the
rhombic lip. Semaphorins, slits, netrins, and TAG1 regulate dorsoventral migration from
the rhombic lip and formation of precerebellar nuclei and pathways. Cyclin D2 and
Unc5h3 regulate the proliferation and rostral arrest of the external granular layer,
respectively. Migration of differentiating granule cells along the glial fibers of the
molecular layer is set up by several molecules, including cell-cycle inhibitors, tubulin
associated proteins, trombospondin, astrotactinn and neuroregulin. The genes controlling
proliferation of Purkinje cell precursors are poorly known, while genes of the reelin
signaling pathway and netrin receptors regulate Purkinje cell migration. Genetic
mechanisms that control the anterior-posterior compartmentalization and foliation of the
cerebellum are beginning to be defined, while little is known about developmental
regulation of the parasagittal zones. Several molecules promoting Purkinje cell and
granule cell survival have been identified. These include growth factors, ion channels,
and neurotransmitters. Proliferation, migration, and survival of the precursors of Golgi
cells, unipolar brush cells, and stellate/basket cells are still scarcely known.
Wang, V. Y., and Zoghbi, H. Y. (2001) “Genetic regulation of cerebellar development”
Nature Reviews, 2:484-491, 2001
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Fig. 32.19 “Four dimensional” (time and space) developmental reconstruction of the
targets of the mossy fiber-parallel fiber-Purkinje cell pathway. Granule cell
precursors (arrows 1-7) migrate from the external granular layer to the definitive granular
layer along the radially oriented Bergmann glial fibers (BGF), leaving in place the
parallel fibers (a, stacked thin rods) in a process of appositional growth. Bergman glia
fibers are processes of the astrocytic Golgi epithelial cells (GEC) situated in the Purkinje
cell (PC) layer. PCD, proximal Purkinje cell dendrite forming spiny branchlets. St,
stellate cells oriented perpendicular to the parallel fibers. Pia, pial membrane; EGL,
external granular layer; ML, molecular layer; PCL, Purkinje cell layer; GL, granular
layer. (Reproduced from Rakic, P., J. Comp. Neurol. 141:283-312, 1972, with
permission).
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Human cerebellar development is not complete at birth.
In humans, the first cerebellar structures develop at approximately 32 days after
fertilization, and development is not completed until long after birth. The cerebellar
cortex of the early embryo has six distinct layers, but this number is ultimately reduced to
three. The cortex begins to differentiate slightly earlier in the vermis, flocculus, and
median sections of the hemispheres than in the lateral hemispheres. By 7 months after
fertilization, the cerebellar nuclei have attained the shape and location they will have in
the adult. At birth, the cerebellar cortex consists of four uneven layers, and the Purkinje
cells and basket cells are weakly developed. The fourth layer (the external granule cell
layer) disappears within the first postnatal year. In humans, full myelination of cerebellar
connections is not complete until the second year of life.
Summary
Comparative anatomy indicates that the cerebellum develops in parallel with both the
motor apparatus and sensory input to the brain and, in mammals, becomes linked with the
cerebral cortex. Ontogenetic development begins with the output neurons, proceeds to the
inputs, and culminates with the interneuronal matrix.
OVERALL SUMMARY
In spite of it’s deceptively simple circuitry, the cerebellum appears to be the most
sophisticated signal processing structure in the brain. Instead of new functions being
localized there, this neural machinery is used to regulate functions that are localized in
other parts of the brain. Through its many mossy fibers and granule cells, the Purkinje
cells are presented with an enormously diverse input that reflects the state of the body,
the state of the environment and the internal state of the brain. Through the training
influence of its climbing fibers, the Purkinje cells then learn to detect the occurrences of
complex patterns of state, which mark the times at which they need to use their powerful
inhibition to shape cerebellar nuclear output in order to regulate populations of neurons in
other parts of the brain. The oldest modules of the cerebellum regulate the motor
commands that orient the eyes and head toward interesting objects in the world around
us. Other modules regulate the motor commands for locomotion, and yet others the
command signals for voluntarily manipulating objects. The newest modules in the
cerebellum, located in the hemispheres, regulate signals in the cerebral cortex that plan,
perceive and solve problems. All of these functions are complex operations, and we still
have much to learn about the mechanisms that support the cerebellum’s many signal
processing operations.
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SUGGESTED READINGS
1. Frackowiak, R. S. J., Friston, K. J., Frith, C. D., Dolan, R. J. and Mazziotta, J. C.
(1997). Functional organisation of the motor system. In: Human Brain Function: pp 243274, Academic Press.
2. Houk, J. (2001). Neurophysiology of frontal-subcortical loops. In: Frontal-Subcortical
Circuits in Psychiatry and Neurology. D. G. Lichter and J. L. Cummings. New York,
Guilford Publications: 92-113.
3. Houk, J. C., Buckingham, J. T. and Barto, A. G. (1996). “Models of the cerebellum
and motor learning.” Behavioral and Brain Sciences 19: 368-383.
4. Ito, M. (1984). The Cerebellum and Neural Control. New York, Raven Press.
5. Liu A, Joyner AL. 2001. Early anterior/posterior patterning of the midbrain and
cerebellum. Annu Rev Neurosci 24:869-896.
6. Middleton, F. A. and Strick, P. L. (1998). Cerebellar output: motor and cognitive
channels. Trends in Cognitive Sciences. 2: 348-354.
7. Mugnaini, E. (2000). GABAergic inhibition in the cerebellar system. GABA in the
Nervous System: The View at Fifty Years. D. L. Martin and R. W. Olsen: 383-407.
8. Thach, W. T. (1998). “What is the role of the cerebellum in motor learning and
cognition?” Trends in Cognitive Sciences 2(9): 331-337.
9. Thompson, R. F. (1986). “The neurobiology of learning and memory.” Science 233:
941-947.
10. Voogd, J. and Glickstein, M. (1998). “The anatomy of the cerebellum.” TINS 21(9):
370-375.
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timing and prediction in the control of reaching.” Neural Computation 11: 565-594.
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