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PROCESSING OF VISUAL INFORMATION IN DIM LIGHT
Functional variability, matched filtering and spike coding
in cockroach (Periplaneta americana) photoreceptors
KYÖSTI HEIMONEN
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100 ms
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REPORT SERIES IN PHYSICAL SCIENCES
Report No. 52 (2008)
PROCESSING OF VISUAL INFORMATION IN DIM LIGHT
Functional variability, matched filtering and spike coding
in cockroach (Periplaneta americana) photoreceptors
KYÖSTI HEIMONEN
Department of Physical Sciences
Division of Biophysics and Biocenter Oulu
University of Oulu
Finland
Academic dissertation to be presented, with the permission of the Faculty
of Science of the University of Oulu, for public discussion in the Auditorium
GO101, Linnanmaa, on December 5th, 2008, at 12 o’clock noon.
REPORT SERIES IN PHYSICAL SCIENCES Report No. 52
OULU 2008 ● UNIVERSITY OF OULU
Opponent
Prof. Eric J. Warrant, Lund University, Sweden
Reviewers
Prof. Andrew S. French, Dalhousie University, Canada
Prof. Eric J. Warrant, Lund University, Sweden
Custos
Prof. Matti Weckström, University of Oulu, Finland
ISBN 978-951-42-8948-4
ISBN 978-951-42-8949-1 (PDF)
ISSN 1239-4327
Oulu University Press
Oulu 2008
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“Nothing changes but the colour changes hue”
“Nothing changes but the channel changes view”
- Midnight Oil (2001) Too Much Sunshine. In: Capricornia -
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Heimonen, Kyösti: Processing of visual information in dim light. Functional
variability, matched filtering and spike coding in cockroach (Periplaneta
americana) photoreceptors.
Faculty of Science, Department of Physical Sciences, Division of Biophysics,
University of Oulu, P.O. Box 3000, FI-90014 University of Oulu, Finland;
Biocenter Oulu, University of Oulu, P.O. Box 5000, FI-90014 University of Oulu,
Finland.
Report Series in Physical Sciences No. 52 (2008).
Abstract
Sensory systems are considered to be optimized for their ecological niche. In
vision this means highly organised regular structure and function, where nearly
identical photoreceptors have graded light responses in order to be able to handle
as much information as possible. Instead, cockroach compound eyes show large
amounts of irregularities in their optics and structure, and unusually long axons.
In this thesis photoreceptors of the cockroach were studied with intracellular
recordings of their light responses, biophysical systems analysis, and modelling
of the relations between the light stimuli and responses. Cockroaches prefer living
in dark or extremely dim environments. However, they have large and complex
compound eyes. The aim of this study was to find out the functional properties by
which the visual system and especially photoreceptors have adapted to cope with,
i.e. to see in, dim light conditions.
The function of photoreceptors was found to vary randomly in many respects,
and the long axons seemed to utilise action potential coding of visual signals.
Through model simulations it was shown that signals of a group of these
functionally variable and spiking photoreceptors, when pooled, could provide
more reliable coding than signals of identical cells of any experimentally
characterised type. This naturally sacrifices spatial resolution. The filtering
dynamics of the photoreceptors is matched to low light intensities and their
temporal resolution does not markedly improve with increasing light adaptation.
Adaptation processes in the photoreceptors saturated near an intensity of about
1000 effective photons/s. These are all both unexpected and novel features of
photoreceptor function. Spatial summation of functionally different
photoreceptors and reduced temporal resolution and contrast coding abilities can
be considered to be permanent optimizations to a dim environment.
Keywords: vision, sensory coding, adaptation, temporal, spatial, resolution.
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Acknowledgements
This thesis has been done at the Department of Physical Sciences, Division of
Biophysics in the University of Oulu. I owe my highest appreciation and deepest
gratitude to my supervisor, and dear friend, Professor Matti Weckström, the head
of the division and biophysics research group. I want to thank him, from the
bottom of my heart, for the opportunity to do this thesis and the excellent
guidance, support, ideas, discussions, and collaboration during the whole process.
Without him this work would still be undone. I also wish to thank all the
professors, especially Jukka Jokisaari, the head of the department, and everybody
else at the department for creating a great scientifically oriented atmosphere.
Working at the department has been a privilege.
I’m thankful to everybody at biophysics for their collaboration and friendship,
and all the brilliant discussions. Especially, I want to thank Esa Luoma for his
help with the technical and computer problems, Iikka Salmela for his help with
the recordings and figures, Panu Kontiokari for programming and running the
simulations, Arto Piironen for programming the Hilbert transform and Mikko
Vähäsöyrinki for his competent help with various scientific problems and tasks. I
also want to thank Mikko Lempeä for his expertise on building several of the
“gadgets” crucial for the experiments and Anja Mäntykenttä for her help with the
practical arrangements.
Some early parts of the thesis were done at the Department of Physiology and
the Department of Biology in the University of Oulu. I’m also grateful to
everybody involved with my work there. Particularly, I want to thank Eero
Kouvalainen for his friendship and exquisitely competent technical assistance,
Mikko Juusola for programming the recording and analysis programs and Pasi
Tavi for his friendship and expert comments on diverse scientific issues.
I also wish to express my appreciation and regard to everybody who
participated in commenting, correcting and improving the manuscript and its
language, but especially to Professors Eric Warrant and Andrew French.
Finally, I wish to express my ultimate gratitude to Leena Inkala for showing
me that there are also other things in life (apart from science) worth experiencing.
Her support, comfort, thoughts and love have helped me to bear myself during
this process.
Katajaranta, November 2008
Kyösti Heimonen
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Abbreviations
A
Amax
c
Cm
DC
gm
ΔI
Δρ
Erev
ERG
FFT
I
Im
ISI
LED
N
n
R
Rm
SD
SNR
τm
UV
V-logI curve
Vm
Vmax
Amplitude (mV) of a light response
Maximum amplitude of the light response
Contrast (=ΔI/I)
Capacitance of the cell membrane
Direct current
Conductance of the cell membrane
Change in the light intensity
Acceptance angle, i.e. the width of the photoreceptor
receptive field at 50% sensitivity level
Reversal potential of the current through the cell membrane
Electroretinogram
Fast Fourier transform
Intensity of light
Current through the cell membrane
Inter-stimulus interval
Light emitting diode
Number of photons
1) Usually number of experiments made or numerical values
used to calculate an average value
2) In the special case of the modified (by Laughlin 1981)
equation of Lipetz (1971) the exponent that is added to
consider the effects of additional sources of nonlinearity
Reciprocal of the light intensity (I) producing a half-maximal
(Vmax/2) amplitude for the light response
Resistance of the cell membrane
Standard deviation
Signal-to-noise ratio, i.e. the power or amplitude of the signal
divided with the corresponding value of the noise
Time constant of the cell membrane
Ultraviolet
A curve, where the maximum amplitude of the photoreceptor
voltage response (Vmax) is plotted in function of the
stimulus light intensity (I)
Voltage over the cell membrane
Maximum amplitude of the light response
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List of original articles
This thesis is based on the following papers, which are referred to in the text by
their Roman numerals I – III:
I
Heimonen K, Salmela I, Kontiokari P & Weckström M (2006) Large functional
variability in cockroach photoreceptors: An optimization to low light levels. J
Neurosci 26: 13454-13462 (+ supplementary material).
II
Heimonen K, Juusola M & Weckström M (2008) Are the linear dynamics of
cockroach photoreceptor responses matched to low levels of luminance? Manuscript.
III Weckström M, Järvilehto M & Heimonen K (1993) Spike-like potentials in the
axons of nonspiking photoreceptors. J Neurophysiol 69: 293-296.
In the first two articles (I, II) the measurements, their analysis and writing were
mainly done by Heimonen. Weckström had crucial influence at the level of ideas
and all the authors participated in writing. In paper I Salmela and Weckström
helped with the recordings and Kontiokari was primarily responsible on the
programming of the simulation. Juusola programmed the original recording and
analysis programs in paper II. The third article (III) is partly based on the
recordings and their analysis in the master thesis of Heimonen (1990) supervised
by Järvilehto. Additional recordings, modelling and writing of the first version
were done by Weckström.
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Contents
Abstract
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Acknowledgements
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Abbreviations
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List of original articles
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Contents
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1 Introduction
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2 Review of the literature
18
2.1 Processing of visual information in photoreceptors ................................ 18
2.1.1 Spatial resolution and spectral sensitivity .................................... 19
2.1.2 Phototransduction and graded light responses.............................. 20
2.1.3 Adaptation .................................................................................... 22
2.1.4 Temporal resolution...................................................................... 23
2.2 Vision in dim light................................................................................... 26
2.2.1 Noise............................................................................................. 26
2.2.2 Temporal and spatial summation.................................................. 27
2.3 Vision of the cockroach........................................................................... 28
2.3.1 Visually guided behaviour of the cockroach ................................ 30
2.3.2 Structure of the cockroach visual system ..................................... 33
2.3.3 Function of the cockroach visual system...................................... 38
3 Aims of the research
45
4 Summary of the material and methods
46
4.1 Animals and preparation ......................................................................... 46
4.2 Recording and stimulation equipment and procedures ........................... 48
4.3 Quality of recordings .............................................................................. 50
4.4 Marking and anatomical identification of cells with spike
responses ................................................................................................. 50
4.5 Localization of recorded photoreceptor responses.................................. 51
4.6 Recordings of photoreceptor impulse and step responses in dark
adaptation ................................................................................................ 51
4.7 Recordings of photoreceptor step responses in light adaptation ............. 53
4.8 Determination of receptive field ............................................................. 53
4.9 Determination of signal-to-noise ratio .................................................... 53
4.10 Determination of frequency response ..................................................... 54
4.11 Evaluation of quantum bump size and shape in light adaptation ............ 55
4.12 Determination of impedance function..................................................... 55
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4.13 Statistical tests ......................................................................................... 55
4.14 Modelling and simulations ...................................................................... 56
5 Summary of the results in the original articles
57
5.1 The spatial filter ...................................................................................... 58
5.2 Properties of responses in dark and light adaptation and their
variability ................................................................................................ 59
5.3 The SNR of contrast coding .................................................................... 62
5.4 The frequency response........................................................................... 63
5.5 The transduction filter ............................................................................. 64
5.6 The membrane filter................................................................................ 67
5.7 The “axon filter” and spike coding ......................................................... 68
5.8 A hypothesis concerning the function of the cockroach visual
system...................................................................................................... 69
6 Discussion
70
6.1 Functional variability of the photoreceptors ........................................... 70
6.2 Is functional variability of photoreceptors optimization to low
light levels? ............................................................................................. 74
6.3 Is filtering of visual information in cockroach photoreceptors
matched to low levels of luminance? ...................................................... 75
6.4 Is the visual information spike coded in the cockroach
photoreceptor axons? .............................................................................. 76
6.5 Are cockroach photoreceptors optimized for vision in dim light? .......... 79
7 Conclusions
81
References
82
Original articles
97
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1
Introduction
Information processing in neurons or neuronal systems is based on electrical
signals produced over and travelling along the membranes of neuronal cells and
chemical (synapses) or electrical (gap junctions) communication between the
neurons. The biophysical and biochemical basis and the physiology in general
behind these processes can be found in many textbooks on neuroscience,
biophysics or physiology (e.g. Johnston & Wu 1995, Koch 1999, Kandel et al.
2000, Hille 2001, Dayan & Abbott 2005). At the level of a neuron this
information processing includes several complex cell physiological and
biophysical phenomena, for example:
1.
2.
3.
4.
Homeostatic control of the cellular environment, e.g. uneven distribution of
the ionic constituents of intra- and extracellular fluids and their transportation
through the membrane, control of Nernst and resting potentials etc.
Generation of electrical signals with the ionic currents through the membrane
via controlled but stochastic closing and opening of the membrane-bound ion
channels.
Coding of the carried information into electrical signals of the cell membrane,
i.e. graded responses (continuous, or analogue, signals) and/or action
potentials (pulse coded or “digitized” signals).
Propagation of the signals along the neuronal cable formed by the membrane.
Consequently the production and propagation of the electrical signals carrying the
necessary information are controlled and their changes actively processed by the
neurons themselves. The roles of the other major cell groups of nervous systems,
collectively called "glial cells" are not completely understood, and are generally
considered to be supportive of neurons. However, they may also be found to
contribute to information processing.
In a sensory neuron the information is harvested from the physical or
chemical nature of the surroundings, not from the signals of other neurons, and
transformed into electrical signals of the cell membrane via the transduction
process (reviewed e.g. by French 1992, Gold 1999, Herness & Gilbertson 1999,
Burns & Baylor 2001, Hardie 2001, Hardie & Raghu 2001, Bhandawat et al. 2005,
Hardie 2006, Lumpkin & Caterina 2007, Vollrath et al. 2007). In a sensory
transduction process the energy released by the absorption of an adequate
stimulus (e.g. a photon) is absorbed by a membrane bound specialized receptor
molecule (e.g. a rhodopsin photopigment). This triggers a biochemical chain of
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reactions called the transduction cascade, which ultimately leads to opening of ion
channels and generation of electrical signals.
The interest to study especially visual systems is partly based on the fact that
for us humans vision is such an essential and central sensory system. The second
good reason to use vision as a target of studies in sensory and neural processing is
more pragmatic. Our ability to control visual inputs, light stimuli, down to their
finest details (direction, wavelength, intensity etc.) makes vision a very benign
and beneficial sensory system from the point of view of biophysical systems
analysis. This ability enables the exact control and recording of both the stimulus
(input) and the response (output). Thirdly, many photoreceptors are fairly large in
size, especially in diameter (to capture light effectively), when compared to most
other sensory cells, and thus accessible to intracellular recording.
In visual systems, here particularly invertebrate photoreceptors, processing of
visual information and membrane responses generated by the visual neurons have
been and are actively studied (e.g. books and reviews: Autrum 1958, Wolken
1971, Järvilehto 1978, Autrum 1981, Land 1981, Laughlin 1981, Shaw 1984,
Weckström 1987, Laughlin 1989, Stavenga & Hardie 1989, Warrant & McIntyre
1992, Juusola 1993a, Uusitalo 1995, Weckström & Laughlin 1995, Juusola et al.
1996, Land 1997, Hardie & Raghu 2001, Vähäsöyrinki 2004, Warrant & Nilsson
2006, Frederiksen 2008). Thus processing of visual information is well known in
several invertebrate species, especially in the blowfly Calliphora, the horseshoe
crab Limulus and lately also in the fruitfly Drosophila.
In the course of animal evolution vision has played a key role in sensing the
physical world (Parker 2004). Accordingly, visual systems have evolved and can
be considered to be well adapted, or even optimized, to their environments and
tasks (Laughlin 1989, Laughlin 1990, Weckström & Laughlin 1995, Laughlin
1996, Warrant 1999, Warrant 2004). This has produced many extraordinary and
even peculiar solutions for the structure and function of the visual system.
Although darkness literally means something one can’t see, some animals have
not lost their eyes while evolving to live in and cope with dim, and to us humans
even dark, conditions. Instead, they have even retained fairly complex visual
systems and visually guided behavioural tasks (e.g. Kelber et al. 2002, Dacke et al.
2003, Dacke et al. 2004, Warrant et al. 2004, Warrant 2008). A fair amount is
known about seeing in dim light (reviewed e.g. by Hess et al. 1990, Laughlin
1990, Warrant 1999, Warrant 2004, Warrant 2006, Frederiksen 2008), but also a
great deal remains a mystery.
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This thesis is based on papers (I, II, III), where the visual system and
primarily the photoreceptor cells of the American cockroach (Periplaneta
americana) have been studied. This was done through intracellular recordings of
the electrical signals of the cell membranes responding to controlled light stimuli
and via biophysical systems analysis of the relations between the stimuli and the
responses. Biophysical modelling of the function of cockroach photoreceptors
and visual system was also utilized. Cockroaches prefer living in dark or
extremely dim environments (e.g. Guthrie & Tindall 1968, Bell & Adiyodi 1982,
Huber et al. 1990a, Huber et al. 1990b, Mote 1990, Halloy et al. 2007). However,
they have retained large and complex compound eyes in the course of evolution
(Butler 1973a, Butler 1973b, Mote 1990). Thus the emphasis of this study has
been placed on revealing the functional features by which their visual system, and
especially their photoreceptors, have adapted to cope with, i.e. to see in, dim light
conditions.
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2
Review of the literature
The information brought into the eye by the incident light is in the form of spatial,
spectral, intensity and polarisation information. In other words the light carries
information about the direction of its arrival, the colour (wavelength or energy of
photons) and the brightness (number of photons) of its source and possibly about
the angle of the plane it is polarized in. In addition, all this information can
change temporally, i.e. the time of arrival can vary, through self-motion, temporal
intensity variation in the surroundings, etc. The processing of this information
takes place in the visual system, where the photoreceptor cells with the optical
structure of the eye form the sensory apparatus. Hence, it is the photoreceptors
(with the aid of the optics) who dictate, which features and parts of the available
light information are going to be captured and transduced into the electrical
signals for the whole neural network of vision to be further processed.
Since this thesis deals with the photoreceptors of an insect, the American
cockroach (Periplaneta americana), the approach taken here is based mainly, if
not almost completely, on the literature on the insect and compound eye
photoreceptors. Even this limited field of vision research is so widely studied that
this review had to be concentrated almost solely on the subjects and viewpoints
covered in the original articles of this thesis (I, II, III). Only what little is known
about the vision and the visual system of the cockroach is reviewed here more
extensively (section 2.3). It is to be noted that there is a huge amount of literature
on the subject of visual information processing, especially when considering also
the vision of vertebrates, which is completely omitted here.
2.1
Processing of visual information in photoreceptors
The word “processing” represents here all the modifications the photoreceptor
executes on the information arriving with the absorbed photons. At first, light
information is absorbed and transduced into information that is in the form of a
changing membrane potential i.e. the light response signals. Then these electrical
membrane signals are carried or propagated along the neuronal cable to the first
synapse. All the processing the photoreceptor performs alters or filters the
information contents of the signals. A “filter” or “filtering” is a widely used way
to describe a certain feature of this processing. It gives a relative measure of
membrane potential amplitude or some other form of scaled strength or power of
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the response as a function of a certain quality of the stimulus, e.g. direction,
colour, speed, frequency etc.
2.1.1 Spatial resolution and spectral sensitivity
Before the biochemical and electrical events in the photoreceptors take place
the optics of the eye process the light information. The main purpose is to focus
the desired portion of the incident light (or photons) into the light absorbing or
sensitive structures of the photoreceptors. The compound eyes are formed of
“eye-units” or “partial eyes” called ommatidia, whose number varies from a few
to tens of thousands between animal species and developmental stages (reviews
e.g. Wolken 1971, Land 1985). Each ommatidium has its own optical system and
photoreceptors behind it. Several extensive reviews and articles are available (e.g.
Kirschfeld 1971, Land 1981, Land 1985, Nilsson 1990, Land 1997, Frederiksen
2008) on the optical arrangement of the different compound eyes (apposition,
neural superposition and diverse optical superposition eyes) and their function.
They are not reviewed any further here, only the structure and optics of the
cockroach apposition eye is covered in more detail later (section 2.3.2).
Basically the optical axes of the ommatidia in a compound eye each have
their own slightly different directions and thus the photoreceptors in them have
their own visual fields (e.g. Land 1981, Land 1997). In a fused rhabdom, like in
the cockroach (Snyder & Horridge 1972, Snyder et al. 1973), all the
photoreceptors inside one ommatidium share the same field of vision. Roughly
speaking it can be thought that there is a mosaic picture formed on the retina of
the compound eye (Kirschfeld 1971), where the grain or “pixel” size is defined by
the visual fields of the ommatidia. Hence, it is the optical arrangement of the
compound eye that mainly forms and dictates the visual field or spatial filter of a
photoreceptor (e.g. Land 1981, Land 1997). The spatial filter is usually presented
in the form of a receptive field or angular sensitivity function, where the scaled or
normalized amplitude of the light response, i.e. photoreceptor sensitivity, is
plotted as a function of the angle of the stimulating light (see Fig. 4 in I). The
angular sensitivity functions are normally considered to have a Gaussian shape,
because of diffraction in a tiny lens. Thus they can be described by a single
number, the acceptance angle Δρ, usually defined as the width (in degrees) of the
Gaussian at 50% sensitivity level.
The optical properties and spatial filtering can not be totally separated from
all the processing of light information taking place in the photoreceptors. To some
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extent the optical properties of the ommatidia are dependent on the
photoreceptors in them, namely the properties of rhabdomeres and pigments in
the cells (e.g. Snyder & Horridge 1972, Snyder et al. 1973, Stavenga 2003a,
Stavenga 2003b, Stavenga 2004). Partially the optics regulates, restricts and
modifies the function of photoreceptors, for instance the spectral contents of the
light reaching the photoreceptors can be (and is) altered.
The next filter that shapes the information in incident light is formed by the
internal properties of photoreceptors themselves. The spectral sensitivity of a
photoreceptor is principally defined by the properties of the photopigment
molecules embedded in the rhabdomeric membrane (e.g. Stavenga & Schwemer
1984, Hardie 1986, Stavenga & Hardie 1989). These visual pigments absorb the
incident photons and start the phototranduction cascade. The probability of the
absorption is dependent on the wavelength or energy of the incident light. This
can be presented in the form of a spectral sensitivity function or spectral filter,
where the scaled or normalized amplitude of the light response (i.e. photoreceptor
sensitivity) is plotted as a function of the wavelength of the stimulating light.
When there are at least two (or more) types of photoreceptors with
photopigments that have different spectral sensitivities in the same animal or eye,
the existence of colour vision is possible. A large number of original papers and
reviews are available on spectral sensitivities and colour vision in insects and
other invertebrates (e.g. Menzel 1979, Hardie 1986, Menzel & Backhaus 1989,
Briscoe & Chittka 2001, Kelber 2006) and there is no need to review them here
any further. However, what little is known about the spectral sensitivity of the
cockroach visual system is given a more detailed presentation later (section 2.3.3).
To summarize this section, it can be said that generally the acuity and spatial
resolution (or spatial filter) is set by the optical properties of the ommatidia and
photoreceptors. It can be described through the receptive field and the acceptance
angle Δρ. On the other hand, the spectral filter of the photoreceptors is mainly
defined by the visual pigments and their spectral sensitivities.
2.1.2 Phototransduction and graded light responses
Each photon absorbed by a photopigment molecule triggers the transduction
cascade, a series of biochemical reactions on the rhabdomeric membrane, and
produces a single membrane voltage response called quantum bump (Fuortes &
Yeandle 1964, Scholes 1964, Dodge et al. 1968, Lillywhite 1977, Lillywhite &
Laughlin 1979, Laughlin 1981, Laughlin & Lillywhite 1982, Laughlin 1990,
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Henderson et al. 2000, Burton 2006). Rushton’s principle of univariance (Naka &
Rushton 1966, Rushton 1972) states that after being absorbed the photons have
equal values, i.e. different colour photons produce similar quantum bumps. The
biochemical events in the transduction cascade of Drosophila are known almost
completely (Hardie 2001, Hardie & Raghu 2001, Hardie 2006, Minke & Parnas
2006). Only the last steps before opening the light-gated ion channels are still not
clear in the smallest details. There is some evidence that the transduction cascades
of other insects and invertebrates might be somewhat different (reviewed by
Hardie 2006), but our knowledge about this is not as detailed. The final steps in
the biochemical reactions of the transduction cascade lead to opening of lightgated ion channels in the rhabdomeric membrane. This allows ionic current to
pass through and causes a voltage change over the membrane, producing the
quantum bump. It is generally hypothesized that each individual quantum bump is
produced by an individual rhabdomeric microvillus (Howard et al. 1987).
When a photoreceptor is illuminated, the quantum bumps generated by
different microvilli propagate along the photoreceptor membrane and sum up to
form a graded receptor potential over the photoreceptor soma membrane (Dodge
et al. 1968, Wong & Knight 1980, Wong et al. 1980, Wong et al. 1982, Juusola et
al. 1994, Juusola & Hardie 2001a, Faivre & Juusola 2008). The size, shape and
temporal properties of the bumps are dependent on the average number of
incident photons per unit time, i.e. intensity of the light. This means that the
phototransduction cascade is modified by light adaptation. Hence, the summation
process and graded responses are changed with increasing light adaptation.
One way to study and describe the summation of quantum bumps is to record
so-called V-logI functions, where the maximum amplitude of the graded response
(Vmax) to some transient stimulus is plotted as a function of the logarithm of the
stimulus intensity (I) (Laughlin & Hardie 1978, Laughlin 1981, Matic & Laughlin
1981). As a whole these curves behave nonlinearly. One reason for this is the
“self-shunting” of the current through the light-gated channels. The depolarising
membrane voltage (Vm) of the cell membrane approaches the reversal potential of
the light current (Erev) and reduces the electromotor force (Vm – Erev) driving the
ionic current. The membrane current (Im) does not grow relatively as much as the
voltage, since
I m = g m (Vm − Erev ) ,
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where gm = membrane conductance. The fact that a relatively smaller portion of
photons is absorbed with an increasing amount of light (i.e. there is a lowering of
quantum efficiency) is one additional source of nonlinearity in the formation of
receptor potential (Smola 1976, Wu & Pak 1978, Laughlin 1981, Howard et al.
1987, Pelli 1990). The change in the quantum bumps themselves, whereby they
get smaller and faster with increasing light adaptation (in addition to the selfshunting effect), is another source of nonlinearity (e.g. Dodge et al. 1968, Smola
1976, Wu & Pak 1978, Juusola & Hardie 2001a, Faivre & Juusola 2008). The
presence of outwardly rectifying voltage-gated channels causing changes in gm
while Vm is altered (Weckström et al. 1991, Weckström et al. 1995, Weckström &
Laughlin 1995) is yet another source.
When only “self-shunting” is taken into account, the V-logI curve can be
fitted with the Lipetz (1971) equation. But when all the additional sources of
nonlinearity are also considered, the additional exponent n is added to the
equation (with self-shunting n is equal to one) (Lipetz 1971, Laughlin 1981). This
gives a modified version of the Lipetz equation:
V = Vmax
( RI ) n
,
( RI ) n + 1
where R = reciprocal of the light intensity (I) producing a half-maximal (Vmax/2)
amplitude for the light response. The exponent n is dimensionless and has values
smaller than one. The smaller the value of n, when fitted to experimental data, the
more nonlinear is the summation of quantum bumps and formation of receptor
potential.
2.1.3 Adaptation
Generally adaptation at the photoreceptor level means the optimization of
sensitivity according to the intensity level of the ambient illumination (reviewed
e.g. by Autrum 1981, Laughlin 1981, Laughlin 1989). There are two basic
mechanisms of adaptation in the photoreceptors. The photochemical adaptation
modifies phototransduction and the electrical properties of the photoreceptor
membrane and is the faster mechanism taking place usually within seconds. The
slower photomechanical adaptation can last several minutes and causes
morphological changes in the photoreceptors.
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In the compound eyes of Dipteran flies the photomechanical adaptation takes
place through the migration of pigment granules inside the photoreceptors
(Kirschfeld & Franceschini 1969). These granules aggregate around the rhabdom
in light adaptation and the mechanism is thus called an intracellular pupil. This
reduces the amount of light travelling inside the rhabdom and thus also the
number of photons absorbed by photopigments (Snyder & Horridge 1972, Snyder
1975, Roebroek & Stavenga 1990b) (cf. section 2.3.3). This again means lowered
sensitivity for light, i.e. more light is needed to produce as large responses in light
adaptation as was produced by dimmer stimuli in dark adaptation. As a result light
adaptation causes the V-logI curve to move towards higher intensities along the
logI-axis (Laughlin & Hardie 1978, Laughlin 1981, Matic & Laughlin 1981,
Roebroek & Stavenga 1990a).
Photochemical adaptation for its part alters the size, shape and latency of the
quantum bumps (e.g. Dodge et al. 1968, Smola 1976, Wu & Pak 1978, Laughlin
1981, Juusola et al. 1994, Juusola & Hardie 2001a, Faivre & Juusola 2008) and
also the properties of the photoreceptor membrane (Weckström et al. 1991,
Weckström et al. 1992, Juusola & Weckström 1993, Weckström & Laughlin 1995,
Krause et al. 2008b). With increasing light adaptation the quantum bumps
generally become smaller and faster, and the cell membrane allows faster changes
in voltage. This not only makes the visual system faster (see the next section
2.1.4.), but also lowers the sensitivity (as mV:s per photon) via smaller bumps.
Again the V-logI curves are shifted towards higher intensities (Laughlin & Hardie
1978, Autrum 1981, Laughlin 1981, Matic & Laughlin 1981, Roebroek &
Stavenga 1990a). Their shape can also change, since the value of n in the
modified Lipetz function (see section 2.1.2) can be altered.
Effectively, the existence of these adaptation mechanisms means that the
functional range of the photoreceptors is always regulated according to the
average intensity of the stimulating light. This enables animals to adapt to and
cope with the very large variations in the ambient illumination (e.g. Warrant 1999,
Warrant 2004, Warrant 2006), even though the range of possible response
amplitudes or voltages is very limited (about 60-70 mV) in any one adaptational
state.
2.1.4 Temporal resolution
Temporal resolution describes how rapid changes in the intensity (contrast coding)
or in any other property of the light can be coded into the changes of the light
23
response signals. It can be evaluated either through recording the impulse
responses of the photoreceptor (Howard et al. 1984, Laughlin & Weckström 1993,
Laughlin 1996, see also 2.3.3) or by a more common approach via linear systems
or white noise analysis in the frequency domain (e.g. French 1980c, Juusola et al.
1994, Burton et al. 2001, Juusola & Hardie 2001a, Niven et al. 2003,
Vähäsöyrinki et al. 2006, Faivre & Juusola 2008, Frederiksen et al. 2008). The
temporal resolution of a photoreceptor is affected and set by the temporal
properties of both phototransduction and the cell membrane (see also Weckström
et al. 1992, Juusola & Weckström 1993, Laughlin & Weckström 1993, Weckström
& Laughlin 1995).
In addition, as discussed above (2.1.3), temporal properties of light responses
can also be influenced and regulated by the level of light adaptation. The
modification of the temporal properties of phototransduction and quantum bumps
on the basis of light adaptation has been shown and described using the white
noise approach in many species: e.g. Calliphora (Juusola et al. 1994, Juusola et al.
1995), Drosophila (Juusola & Hardie 2001a, Juusola & Hardie 2001b, Niven et al.
2003), other Dipteran flies (French 1979, Burton 2006), locusts (Kuster & French
1985, Faivre & Juusola 2008), the bee Megalopta (Frederiksen et al. 2008) etc. In
all these studies it has been shown that phototransduction speeds up with
increasing illumination, meaning faster bumps with shorter latencies and faster
contrast coding. It is also evident on the basis of these studies that, although the
amplitude of bumps gets smaller with increasing light adaptation, the contrast
gain increases. This all means that the signal-to-noise ratio (SNR) of the
photoreceptor light responses improves with increasing levels of ambient light,
and so does the information capacity (information carried by the responses in
bits/s) as well. This even occurs in the nocturnal bee Megalopta, though it
normally prefers being active in dim or dark conditions.
In addition to the above described changes in phototransduction, all the
voltage responses, quantum bumps and graded responses are further modified or
filtered by the properties of the cell membrane (Weckström et al. 1991,
Weckström et al. 1992, Laughlin & Weckström 1993, Weckström & Laughlin
1995, Niven et al. 2003). In the first place, the passive properties of the membrane,
the resistance (Rm) and the capacitance (Cm), define the time constant (τm) of the
membrane:
τ m = RmCm .
24
Secondly, when the membrane voltage (Vm) changes, the voltage-gated ion
channels (most often K+ channels) in the soma membrane further modify the
responses by altering the resistance, or conductance (gm = 1/Rm), of the membrane.
Basically, the increased opening of these channels with the depolarisation of the
photoreceptor membrane in light adaptation increases gm (or lowers Rm) and
shortens τm allowing faster modifications of the membrane potential. The
membrane impedance (Rm as a function of frequency) has also been evaluated
with the white noise approach at different voltages (Weckström et al. 1992,
Juusola & Weckström 1993). It has also been shown by this method that the
photoreceptor membrane bandwidth increases, and the upper cut-off frequency
rises towards the higher frequencies, with increasing depolarisation. The
dependence of gm on Vm can be more exactly modelled with the aid of HodgkinHuxley-type equations (Niven et al. 2003, Kauranen & Weckström 2004,
Vähäsöyrinki 2004). This modelling can also be utilized in evaluating the
evolutionary and adaptive effects, and also of the different channels types, on the
coding properties of the photoreceptors, and also in other neurons. Recently it has
been shown that the properties of the voltage-gated K+ channels responsible for
regulating the membrane impedance can also be directly regulated or affected by
the phototransduction cascade (Krause et al. 2008b). The photoreceptor voltage
signals are even further modified by cable effects while propagating along the
photoreceptor membrane (Johnston & Wu 1995, Koch 1999). When different
types of voltage-gated channels exist in the output part of the photoreceptors, like
in the axons of honeybee drone (Coles & Schneider-Picard 1989, Vallet et al.
1992, Vallet & Coles 1993), the temporal properties of the signal conduction can
be modified and further enhanced.
To summarize this section, the temporal resolution of photoreceptors is
defined first by the phototransduction cascade producing the photocurrents
through the light-gated ion channels. Secondly, it is modified by the current-tovoltage conversion, which is shaped by the membrane impedance, i.e. both by the
properties of the passive membrane and the voltage-gated ion channels. In
addition, light adaptation plays a key role in adjusting not only the temporal
properties of the phototransduction, but also the membrane filter.
It is also useful to note that photoreceptors code all these above described
changes to their membrane potential (sections 2.1.1-2.1.3) without relaying any
information in the voltage signal about the properties of light that are actually
changing (intensity, direction, colour etc.). To determine whether the change was
spatial, spectral or temporal is a task for the whole neural network of vision and
25
the brain. This can be done only by comparing the responses from different
photoreceptors. There is also ample information available about the processing of
visual information beyond the photoreceptors, i.e. in the optic ganglia of the
compound eyes (e.g. (Shaw 1984, Laughlin 1987, Stavenga & Hardie 1989,
Juusola et al. 1995, Uusitalo 1995, Egelhaaf & Warzecha 1999, Egelhaaf et al.
2002, Egelhaaf 2006, Strausfeld et al. 2006). They are not reviewed here any
further, except in the case of what is known about this in the cockroach (presented
later in section 2.3.3).
2.2
Vision in dim light
Extensive reviews on dim light vision, covering both vertebrate and invertebrate
solutions for seeing in low illumination, do exist (Land 1981, Hess et al. 1990,
Laughlin 1990, Warrant 1999, Warrant 2004, Warrant 2006, Frederiksen 2008,
Warrant 2008) and there is no need to repeat them here. Only some major points
and problems for dark vision, concerning especially the case of the cockroach, are
reviewed here. The known specialisations for dim light vision in the cockroach
are given a more detailed treatment later (sections 2.3.2 and 2.3.3).
2.2.1 Noise
Seeing or reliable coding and processing of visual information in a dim or
dark environment is a self-contradictory task, since the information carried by
dim light is unreliable by nature (reviewed e.g. by Warrant 2006, Frederiksen
2008). This is because in low light conditions the light signals themselves are
severely contaminated by photon shot noise. The quantum nature of light
(photons) leads to random (Poisson-distributed) arrival of photons at any given
point of space. When the number of incident photons (N) in a given time interval
and light intensity (I) is low, the variance (due to randomness) of incident photons
(√N or √I) is large in relation to the number of incident photons itself. Thus the
dimmer the environment the smaller is the SNR of the light signals:
SNR =
N
N
= N =
I
I
= I .
From the viewpoint of photoreceptors this is referred to as extrinsic noise that is
due to the light source. A second unavoidable source of noise in visual processing
is the noise generated by the phototransduction cascade. This in turn is referred to
26
as the intrinsic or transducer noise (Lillywhite & Laughlin 1979, Laughlin &
Lillywhite 1982), which, by its nature, is also Poisson-distributed random shot
noise. Variations in the biochemical events of the transduction cascade produce
transducer noise by causing the quantum bumps, even in a given state of
adaptation, to have different latencies, amplitudes and durations (see also Wong et
al. 1980, Laughlin 1990, Henderson et al. 2000, Juusola & Hardie 2001a, Faivre
& Juusola 2008). This means that the membrane signals of any photoreceptor in
dark or extremely dim conditions are unavoidably noisy.
To be able to see in dim light one has to somehow overcome the noisiness of
the light responses. There are two basic ways to do this (reviewed by Laughlin
1990, Warrant & McIntyre 1992, Warrant 1999, Warrant 2004, Warrant 2006,
Frederiksen 2008). The first is temporal summation via integrating or summing
the incident photons for a longer time. The second is spatial summation, by
collecting light from a wider area in space.
2.2.2 Temporal and spatial summation
Basically, the subject of temporal summation at the photoreceptor level has
already been covered earlier (section 2.1.4). To recapitulate, it can be said that
while temporal resolution is improved with light adaptation, the reverse is also
true, i.e. temporal summation increases with dark adaptation via slower
phototransduction (e.g. Kuster & French 1985, Juusola et al. 1994, Juusola &
Hardie 2001a, Faivre & Juusola 2008, Frederiksen et al. 2008) and a slower
membrane (e.g. Weckström et al. 1992, Juusola & Weckström 1993, Laughlin &
Weckström 1993, Weckström & Laughlin 1995). Nocturnal, crepuscular and darkactive animals usually have slow impulse responses (Howard et al. 1984,
Laughlin & Weckström 1993, Laughlin 1996) and slow and large quantum bumps
(reviews e.g. Laughlin 1981, Laughlin 1990) when dark adaptated. This means
both long integration times of light responses and a high gain of
phototransduction. However, many diurnal or crepuscular species seem to have
almost as slow light responses when dark-adapted as the more nocturnal species
(Howard et al. 1984, Laughlin & Weckström 1993).
Spatial summation strategies include two basically different approaches
(Warrant 1999, Warrant 2004, Warrant et al. 2004, Warrant 2006). The first is the
possession of large receptive photoreceptor fields via various means, and the
second is neural summation or pooling from several photoreceptors. Most
nocturnal insects use the former and have optical superposition eyes (Laughlin
27
1990, Warrant 1999, Warrant 2004, Warrant 2006). This is because the
superposition compound eyes have specialized optical and structural features to
gather or spatially pool light efficiently. For example the moth Deilephila elpenor
is known to be able to fly and even see colors in star light intensities (Kelber et al.
2002). The clear zones of superposition eyes below the lenses and in front of the
photoreceptors, assisted by the migrating pigments, make it possible to collect
light through several lenses for each of the photoreceptors. In dark adaptation the
screening pigments between the ommatidia are removed enabling massive spatial
summation of light. In light adaptation in some species the ommatidia can again
be optically isolated, when the pigments migrate back between them, and good
spatial resolution is enabled.
On the other hand, apposition eyes, like those of the cockroach (TrujilloCenoz & Melamed 1971, Wolken 1971, Butler 1973b), are generally considered
to be badly suited for vision at low luminance (Laughlin 1990, Warrant et al. 2004,
Warrant 2006, Warrant 2008). Large dark-adapted receptive fields (e.g. Butler &
Horridge 1973a, Warrant et al. 2004) and other structural specialisations (e.g.
Butler 1973a, Butler 1973b, Greiner et al. 2004a, Greiner 2006, Warrant 2006) for
collecting light effectively for each of the ommatidia, improve photoreceptor
sensitivity. On the other hand, these specialisations are probably not even nearly
enough achieve proper night or dim light vision. Yet, in addition to cockroaches,
there also seem to be other exceptions. Some apposition-eyed bees, wasps and
butterflies are also known to have a nocturnal way of life (Warrant et al. 2004,
Warrant 2008). Probably the only way left for these animals to have reliable
vision in low light conditions is neural pooling, summation of light information
from several photoreceptors (see also Warrant 1999, Warrant 2006). On the basis
of anatomical findings (e.g. Ribi 1977, Greiner et al. 2004b, Greiner et al. 2005))
this is thought to happen in the first synaptic area of the visual tract, the lamina,
but the electrophysiological evidence for this is still lacking.
2.3
Vision of the cockroach
Among winged insects (Insecta; Pterygota) cockroaches form one of the
phylogenetically oldest groups. The oldest fossil discoveries are close to 400
million years old (Kambhampati 1995). The suborder Blattodea (or Blattaria;
cockroaches) forms an order (Dictyoptera) together with the Mantodea (praying
mantises) and includes several lower taxonomic groups and all together over 4000
species (Bell 1990, Kambhampati 1995, Grandcolas 1996). The particular species
28
studied in this thesis, i.e. the American cockroach (Periplaneta americana),
belongs phylogenetically, according to the fossil records, morphology, DNA
mapping and breeding strategy, to one of the oldest (Blattoidea; Blattidae;
Blattinae) of the taxonomic groups among the Dictyoptera.
Cockroaches are widely studied and there are several books written on
cockroach biology, ecology, evolution, physiology and neurobiology (e.g. Guthrie
& Tindall 1968, Bell & Adiyodi 1982, Huber et al. 1990a, Huber et al. 1990b,
Bell et al. 2007). Particularly the American cockroach is a widely used
experimental animal, but not really very much is known about its vision,
especially in the sense of what the animal needs or uses it for. On the basis of
studies on its behaviour and sensory systems, it can be readily concluded that the
other sensory systems, not vision, are primary sources of information for
cockroaches. The main sensory systems controlling their behaviour and
locomotor activity, especially the widely studied escape behaviour, are the
different mechanosensory organs and cells in their cerci (e.g. Camhi 1980, Camhi
& Levy 1989, Levi & Camhi 2000), antennae (e.g. Burdohan & Comer 1996, Ye
& Comer 1996) and legs (e.g. Zill 1990, Zill & Seyfarth 1996), and additionally
the chemosensory system also mainly located in the antennae (e.g. Seelinger 1990,
Willis & Avondet 2005, Willis et al. 2008). Probably the most preferred form of
locomotion for Periplaneta is walking or running and following a wall or some
other similar surface by tapping or sweeping it constantly with the antenna
ipsilateral to the surface (Camhi & Johnson 1999, Cowan et al. 2006). The escape
responses are primarily evoked by air movements and vibrations sensed by the
cerci (Camhi 1980, Camhi & Levy 1989) or unpleasant contacts with the
antennae (Comer et al. 2003, Ye et al. 2003). On the other hand, Periplaneta is
one of the fastest runners (in relation to its size) in the world (Camhi & Johnson
1999) and they can also fly (Goldstein & Camhi 1988, Libersat & Camhi 1988),
though only weakly and only in high enough temperatures. So, even though
mechanosensory information probably plays a crucial role in performing these
functions, in principle these kinds of behaviours could benefit from seeing and the
ability to detect motion.
Consequently, on the basis of what is known, while performing its usual
behaviour in the conditions where the cockroach normally prefers to live, the
visual system seems to be some kind of secondary or aiding sensory system for
the primary or most important mechano- and chemosensory systems. However, a
fair number of studies have been published about cockroach vision (reviewed by
Mote 1990). Cockroaches have large, structurally and functionally complex,
29
compound eyes (e.g. Butler 1973a, Butler 1973b, Butler & Horridge 1973a,
Butler & Horridge 1973b, Heimonen 1990, Mote 1990, Heimonen 1999), and
large ocelli (e.g. Mizunami 1994, Mizunami 1995a). It is possible to ask, why do
“creatures of darkness” like cockroaches have large eyes and what do they need
and use their vision for? How and what, if anything, is it possible to see in the
dim and dark conditions they mostly prefer to live in?
2.3.1 Visually guided behaviour of the cockroach
To start with, a further question can be asked: “Is there any visually or light
guided behaviour in the cockroach?” A short answer on the basis of what has been
known already for a fairly long time would be: “Yes, they regulate their daily
locomotor activity rhythm on the basis of visual input via the compound eyes”
(e.g. Roberts 1965, Lipton & Sutherland 1970, Ball 1971, Rivault 1983, Mote
1990, Page 1990). They are most active for a few hours after the dark period
commences and sometimes (or some individuals) also before or after the light
period sets in. Large compound eyes, looking simultaneously almost everywhere
in the surroundings of the cockroach (Butler 1973a), would not be fundamental
for the regulation of circadian rhythms. Smaller eyes, or ocelli, would be quite
enough. Since the large eyes exist, it seems that it is a task for the green sensitive
photoreceptors in the compound eyes to provide information for the circadian
behavioural rhythms (Mote 1990).
The next question would be: “Are cockroaches afraid of light?” It has long
been known that several cockroach species show negative phototaxis, i.e. they
prefer dim and dark environments over brightly illuminated ones (e.g. Guthrie &
Tindall 1968). It has also been shown that cockroaches avoid illuminated spots
when walking on an arena (Kelly & Mote 1990a). This avoidance is colourdependent, and on bright backgrounds cockroaches maximally avoided (ca. 80%
of trials) short wavelength light (400 nm). In contrast, on dim backgrounds longer
wavelength lights (540-570 nm) were most strongly avoided, though only ca.
40% of trials provoked avoidance. In addition, it has been shown that visual input,
such as a flash of light, in conjunction with an air puff directed to the cerci, can
modify preferred escape directions of the cockroach (Riemay 1984). But the
escape itself is evoked by air currents or puffs, not by the light alone. Recently,
while studying social aggregation behaviour and the changes robots can induce to
this behaviour (Halloy et al. 2007), it was also shown that Periplaneta is not
afraid of light and can even learn to prefer more luminous places over the dimmer
30
ones. Although innately most cockroaches aggregated below dim shelters in the
course of time (not immediately, put in tens of minutes), they could be induced to
change this collective behaviour. As soon as pheromone flavoured robots among
them were programmed to choose more brightly illuminated shelters over the dim
ones, the cockroaches did so too. So, cockroaches are not afraid of light as such,
but they do normally prefer dim or dark places, if given a chance to choose.
The third basic question could be: “Are cockroaches ever actively interested
to follow or act on any visual or light signals?” Already over 50 years ago it was
reported (Autrum & Stöcker 1952, Autrum 1958), while studying mostly flies,
that Periplaneta has an optomotor turning response to spatial frequencies up to
about 10 Hz. The existence of this optomotor response has later been confirmed
(Kettunen 1994, Voutilainen 1996), even though the results were slightly different
and not that clear. (Unfortunately neither of these studies has been published on
any international forum.) Kettunen (1994) reported that in about 60-80% of trials
tethered and walking male cockroaches had a positive optomotor response
immediately after the stimulus was started. Her results were probably seriously
contaminated by mechanosensory cues, i.e. vibrations of experimental setup that
primarily governed the behaviour. With a more sophisticated setup Voutilainen
(1996) showed that in about 90% of trials cockroaches (again walking and
tethered) have a positive optomotor turning response, when stimulated with
spatial frequencies between 0.5 and 5 Hz. The response was weaker (70-80%) at
10 Hz, and with frequencies of 0.1 and 15 Hz there were no significant
differences with the motionless controls. The positively responsive frequency
band (0.5-10 Hz) was the same at all intensities of background light tested
(ranging 3 log units) except the dimmest, where the highest frequency with
positive optomotor responses was 5 Hz (frequencies between 5 and 10 Hz were
not tested). The existence of the optomotor response implies an ability to see at
least slow frequency motion. On the other hand, flies are very well known to be
able to see fast motion (e.g. Egelhaaf & Kern 2002), but still choose to have an
optomotor response only to slow spatial frequencies (1-10 Hz) while walking
(Pick & Buchner 1979). Maybe this could be true also for the cockroach?
In one study (Mizunami et al. 1998) it was reported that Periplaneta can
localize itself in a scene and learn to find a place according to visual landmarks.
In this study cockroaches were first placed on a hot (44-47 °C) black arena with a
cool (17-20 °C) white place. After a few trials they learned to find this cool white
place much faster than originally, or in the case when the cool place was black. In
the second part of the study the walls of the arena were decorated with visual
31
landmarks and the arena itself, including the cool place, was completely black.
Also in this case most of the cockroaches (ca. 80%) were able to learn to find the
cool place faster after a few trials. Moreover, the individuals that learned this
were lured to a wrong place, when the landmarks were turned to a different
position.
Until quite recently not very much was known or published on any other
forms of visually guided behaviour in the cockroach except these above
mentioned studies. However, during the last five years a few more visually guided,
or at least aided, forms of behaviour have been described. Maybe most
importantly some evidence is starting to accumulate on how vision in Periplaneta
is used to guide and aid the antennae equipped with the mechano- and
chemosensory abilities to find their targets. Studies (Ye et al. 2003, Kwon et al.
2004, Lent & Kwon 2004) show that cockroaches can see and localize objects of
interest, if motivated to do so, and that they can even learn to associate visual
stimuli with other sensory cues.
At first the influence of visual inputs on antennal reorientations and the
directions of escape evoked by mechanically touching the antennae were studied
(Ye et al. 2003). On the basis of only monitoring freely behaving and interacting
cockroaches (and later tethered ones) it was deduced that vision is used to target
the antennae towards moving targets of interest like other approaching
cockroaches or presented artificial targets. In additional tests with tethered
cockroaches, the escape direction (evoked by an unpleasant touch of an antenna)
was affected by the angle form which the antenna was touched. Therefore, since
the antenna is directed by using visual cues, vision has an indirect effect on the
escape direction. In addition, it was found that covering the compound eyes did
not alter the directionality of the escape directly, but it did shorten the average
length of the escape run and reduced the typical speed of running. The shadeinduced pause or termination in escape running has already been reported earlier
(Okada & Toh 1998), and was observed to be mediated via the compound eyes
and to take place even in very low background light intensities.
Secondly, an association between visual and chemosensory cues has also
been reported (Kwon et al. 2004, Lent & Kwon 2004). Normally, cockroaches
actively reach something with their antennae only on the basis of attractive smells
(food, pheromones etc.). But they can also be taught to associate this smell with a
light signal. After this association is formed they move the antennae toward a
light stimulus only. However, it has been noted (and stressed) that lights alone
32
were almost never interesting for untrained animals - they became attractive only
after an association with the smell of food was established.
Most of these studies of visually guided behaviour in cockroaches have been
done in bright light, because of the demands of video recording. Only a few of
them (Kelly & Mote 1990a, Kettunen 1994, Voutilainen 1996, Okada & Toh 1998)
even distantly, address questions about the ability or performance of cockroach
vision in dim light, but they show that cockroaches use and need their vision for
some behavioural tasks. It also has to be noted that the variation in the behaviour
of individual American cockroaches has, on several occasions (e.g. Lipton &
Sutherland 1970, Kelly & Mote 1990a, Kettunen 1994, Voutilainen 1996), been
reported to be very large. This makes them a very demanding target for
behavioural experiments.
2.3.2 Structure of the cockroach visual system
The compound eyes of the cockroach Periplaneta are of the apposition type
(Wolken 1971, Butler 1973b), phylogenetically and morphologically the oldest
type of compound eyes. The other basic types of compound eyes, the
superposition and neural superposition eyes, evolved when cockroaches had
already existed for a time (Nilsson 1989, Shaw 1989, Shaw 1990, Nilsson &
Kelber 2007).The ommatidia of the cockroach eye, like in all apposition eyes, are
optically isolated and completely surrounded by the screening pigment cells,
which uncommonly also reach between the lenses, i.e. the facets (Wolken 1971,
Butler 1973b). This is why, in this special case, the amount of light reflecting out
of the eyes is extremely low and the compound eyes are uniformly black.
Cockroaches are hemimetabolous and in an eye of an individual Periplaneta
the number of ommatidia increases during growth from about 50-100 to about
2000-3500 (Nowel 1981, Stark & Mote 1981). This increase is almost continuous
(not only during moulting) and takes place at the ventral, frontal and dorsal rims
of the eyes (Nowel 1981). Also the size of all the ommatidia increases during this
development. The number of ommatidia in the eye of an adult cockroach varies
individually and correlates with the size of the individual (Füller et al. 1989).
Consequently the reported number of ommatidia also varies greatly between
studies: 2000 in females and 3000 in males (Walther 1958b); an average of about
2000 (Wolken 1971); over 2000 (Stark & Mote 1981); an average of about 3500
(Nowel 1981). According to the most thorough study (Füller et al. 1989), there is
on average 4258 ommatidia per eye in males and the variation between individual
33
eyes is large (3313-5707). The two eyes of a cockroach may also have significant
differences in the number of ommatia, e.g. 4495 in the right and 4013 in the left
eye.
There are also other kinds of unusual variations in the structure of the
Periplaneta compound eye. Usually the lenses of insect compound eyes are
organised in regular rows of regular hexagons (e.g. French et al. 1977, Stavenga
1979, Land 1981, Laughlin 1981) and this is thought to be imperative for the
proper function of the eye. In Periplaneta some roughly regular rows of nearly
regular hexagonal lenses can be found, but in many places these rows end at
irregular clusters of irregular polygons of different sizes and shapes (Butler
1973a). Periplaneta is not the only cockroach where this kind of uncommon
variation in lenses is reported. Gromphadorhina portentosa, another large dark
active cockroach, is also reported to have this irregular variation in the lenses of
its compound eyes (Mishra & Meyer-Rochow 2008). In addition to the variation
in sizes and shapes of the lenses, the angles between the optical axes of the
ommatidia vary in Periplaneta to a surprising extent (Butler 1973a). Both vertical
and horizontal angles vary about 10-fold (1-10 degrees). The largest angles are
located in the peripheries of the ventral region of the eye and the smallest in the
central part of the dorsal region. In comparison, in the blowfly the corresponding
variation is between 1,5 and 4,6 degrees (Burkhardt et al. 1966, Järvilehto 1985).
On the basis of the extent, shape and angles between optical axes the total
visual field of the compound eyes of Periplaneta is very wide-ranging (Butler
1973a). At its widest it is 240° in the horizontal and 198° in the vertical plane per
eye. Hence, the visual fields of the eyes significantly overlap: dorsally by 40°,
anteriorly by 65° and posteriorly by 56°. This enables the animal to see almost its
whole surroundings simultaneously and possibly even to have binocular vision in
the areas of overlap.
The optic lobe of the cockroach resembles that of other insects and includes a
retina and three optic ganglia: the lamina, medulla and lobula (Ribi 1977, Mote
1990). The retina is formed by the screening pigment cells and photoreceptor
somata, which mainly send their axons into the 1st synapse in lamina. Some of the
photoreceptors and all the monopolar neurons of the lamina terminate in the
medulla. The structure of the retina is fairly well known (Butler 1971, TrujilloCenoz & Melamed 1971, Wolken 1971, Butler 1973b, Ferrell & Reitcheck 1993),
but already the retina-lamina connections by photoreceptor axons, and the
structure of lamina, are much less characterized (Ribi 1977, Ernst & Füller 1987)
and leave many questions open. Concerning the structures and connections from
34
lamina onwards only some scattered information is available (Kelly & Mote
1990b, Mote 1990).
Retina
The retinal ommatidium of the cockroach Periplaneta is composed of a
corneal lens, a crystalline cone made of four cells of Semper, and eight retinula or
photoreceptor cells, which each have a light sensitive rhabdomere i.e. the
microvilli (Trujillo-Cenoz & Melamed 1971, Butler 1973b). The depth of the
lenses and crystalline cones is about 150 μm and the retinula cell layer is 250-350
μm thick. The diameter of the ommatidium is 30-35 μm distally, but narrows
down to 5-10 μm proximally, close to the basement membrane. The rhabdom,
comprised of eight rhabdomeres, is of the closed type, i.e. the rhabdomeres form a
joint light guide with a common optical axis (Snyder & Horridge 1972, Snyder et
al. 1973). The overall anatomy of ommatidia is similar all over the retina, but the
sizes of the photoreceptor cells vary between different locations in the retina
(Wolken 1971). The diameter, and thus also the cross- sectional area, of the
rhabdom below the crystalline cone also varies between individual ommatidia
(Ferrell & Reitcheck 1993). When compared with the day-active blowfly
Calliphora the area of the cross-section of the rhabdom is on average about five
times larger in the night-active cockroach Periplaneta (Wolken 1971). The crosssectional area of a rhabdom stays quite constant while advancing distally to
proximally, and only just before the basement membrane does it become smaller
and disappear (Trujillo-Cenós and Melamed 1971, Butler 1971). In contrary, the
cross-sectional area of most individual rhabdomeres changes while advancing
along the rhabdom (Butler 1971). In every ommatidium it is distally large in three
of the photoreceptors and becomes smaller proximally, but this is the opposite in
the other three cells. Every ommatidium has one photoreceptor, where the
diameter of the rhabdomere is comparatively small for the whole length of the
rhabdom and one where it’s relatively large. According to Butler (1971) it is also
possible to number and identify the individual photoreceptors in an ommatidium
according to the order with which their rhabdomeres appear in the cross-sectional
serial sections. Apart from having a fairly large diameter and length, the
cockroach rhabdom has one more special structural feature, namely, it also has a
cup or goblet-like structure at its distal end that surrounds the proximal crystalline
cone (Trujillo-Cenoz & Melamed 1971, Butler 1973b). This structure on its own
35
ensures that the light captured by the optics is also transmitted to the rhabdom and
thus enhances the light-collecting power of the ommatidium.
As first described in locust (Horridge & Barnard 1965), cockroaches also
possess a vacuolar structure called the palisade, which is formed around the
rhabdom when photoreceptors are dark adapted (Butler 1971, Butler 1973b,
Ferrell & Reitcheck 1993). During light adaptation the vacuoles move further
away from the rhabdom, which is instead enveloped by migrating pigment
granules inside the photoreceptors. The functional correlates of this structural
change are described in the next section (2.3.3).
Retina-lamina projection
Deviating from what is generally found in compound eyes, Periplaneta has
below the retina two basement membranes (or basement-membrane like
structures), which are located 40-130 μm apart from each other in the central parts
and connected to each other at the rims of the eyes (Trujillo-Cenoz & Melamed
1971, Ribi 1977, Ernst & Füller 1987). When the rhabdomeres in the
photoreceptor somata disappear little before the 1st basement membrane, the
photoreceptors start to resemble axons (Trujillo-Cenoz & Melamed 1971, Butler
1973b). Between the basement membranes the eight axons leaving the retina from
one ommatidium form a bundle enveloped by a glial sheath (Ribi 1977).
Also contrary to most insects, the distance between the retina and lamina is
long (Butler 1973b, Ribi 1977, Ernst & Füller 1987, Mote 1990). The direct
distance between the proximal retina and distal lamina is at its shortest 200-250
μm (Ribi 1977, Ernst & Füller 1987) and the photoreceptor axons on average are
even longer: ca. 1 mm (Butler 1973b) - 750 μm in the dorsal area and up to 1500
μm in the ventral area of the eye (Ernst & Füller 1987).
According to Ribi (1977) there are two basic types of photoreceptor axons.
The more common type synapses in the lamina and some even longer axons
terminate directly in the medulla. The axons from each ommatidium penetrate
first the basement membranes and the so called fenestrated layer (dense tracheal
network) in bundles of eight. Then they reorganize into bundles of 6-20 axons
before entering the lamina. The longer axons travel directly through the lamina.
The bundles of eight axons below the retina always have one thin axon (diameter
0.8-1.5 μm) and seven thicker ones (2.0-4.4 μm). The axons terminating in the
lamina have a diameter between 1.5 and 2.0 μm and the axons passing through
the lamina are always thinner (0,8-1,5 μm). Axons leaving the retina in about the
36
same area also usually terminate in approximately the same area in the lamina,
but there are also axons deviating from this rule. There are no regularly repeating
structures (like the lamina cartridges in the flies) in the lamina of the cockroach.
The somata of the monopolar cells are situated distally to the lamina. Monopolar
cells are postsynaptic to the photoreceptors and have broad dendritic arborisations
in the lamina. This is also typical for many other nocturnal insects and is thought
to indicate pooling or summation of many photoreceptor signals onto the same
second order neuron (Greiner et al. 2004b, Greiner et al. 2005, Greiner 2006,
Warrant 2006, Warrant 2008).
Ernst and Füller (1987) obtained similar results to Ribi (1977), but presented
some more detailed results and also some differing discoveries. They found that
all the axons have constrictions (diameter only 0.9-1.9 μm), when they penetrate
the basement membranes. The diameter of the axons was found to be 2.1-5.5 μm
between the basement membranes and 1.5-5.0 μm proximally to them. They also
describe slightly different reorganisation of axons into bundles before the lamina,
but don’t mention the numbers of axons in these bundles. It can also be noted that
the organization of the axons is such that the degree of retinotopy in their
projection to the lamina cannot be learned without marking individual axons. The
only regular structures in the lamina are the longer photoreceptor axons that pass
through the lamina with constant distances to each other.
Unusual irregularities in the structure
When summing up the structure of the visual system of the cockroach, two things
are highlighted. First of all, it is obvious that the structure shows several
adaptations to a nocturnal or dark-active life style (Trujillo-Cenoz & Melamed
1971, Wolken 1971, Butler 1973a, Butler 1973b, Ribi 1977, Mote 1990, Ferrell &
Reitcheck 1993). Secondly, it as evident that the arrangement of the compound
eyes includes numerous irregularities and oddly random variations (Butler 1971,
Butler 1973a, Butler 1973b, Ribi 1977, Ernst & Füller 1987, Füller et al. 1989,
Mote 1990, Ferrell & Reitcheck 1993), which can be described as “structural
noise”. It is not only Periplaneta that has these irregularities, but another
cockroach is also reported to have them (Mishra & Meyer-Rochow 2008). Hence,
this might be a more common feature among the cockroaches, at least the
nocturnal ones. However, irregularity is highly unusual for visual systems, where
regularity or high order systematic organisation is thought to be of fundamental
37
importance to the performance of vision (e.g. French et al. 1977, Stavenga 1979,
Land 1981, Laughlin 1981).
To summarize these irregularities and variations in Periplaneta one can start
with the large variation in the number of ommatidia per eye between adult
individuals, and even between the two eyes of a single individual (Walther 1958b,
Wolken 1971, Nowel 1981, Stark & Mote 1981, Füller et al. 1989). In addition,
the shapes and sizes of lenses in an eye vary greatly as do the angles between
optical axes (Butler 1973a). The diameter of the rhabdom varies between the
ommatidia (Wolken 1971, Ferrell & Reitcheck 1993), as does the length of the
rhabdom (Wolken 1971, Butler 1973b) and the size of individual photoreceptors
and their rhabdomeres (Butler 1971). Below the retina the irregularities continue.
The length and diameter of the photoreceptor axons vary greatly (Ribi 1977, Ernst
& Füller 1987). The size, shape and broadness of pre- and post synaptic structures
in the lamina differ from each other between cells (Ribi 1977). The number of
axons in a bundle entering the lamina varies (Ribi 1977, Ernst & Füller 1987).
Numbers of certain cells in the optic lobes, when compared between eyes, are
different (Füller et al. 1989), and so on. It is difficult to see the reason for these
irregularities and seemingly uncontrolled variations, and it seems obvious to ask
why they exist. What can actually be seen with a visual apparatus like the eye of
the cockroach?
2.3.3 Function of the cockroach visual system
The cockroach compound eye is classified as a “slow” compound eye on the basis
of the flicker fusion frequency, measured from extracellular responses to repeated
light flashes (Autrum 1958). In this same study several other insect species were
also classified on the same grounds. It was concluded, though not with this
terminology, that diurnal and crepuscular animals active in dim or dark
environments have “slow” vision, i.e. slow light responses, and integrate light
stimuli over long time periods. In contrast, diurnal insects active in bright light,
especially flying insects, were found to have “fast” vision.
Temporal integration
In another comparative study with several insect species, based on recordings of
dark and light adapted impulse responses and their time-to-peak values, it was
shown that when dark adapted all species are more or less as slow (Howard et al.
38
1984), with long integration times. Only houseflies (Musca domestica) were
found to be faster than locusts (Locusta migratoria). Astonishingly, for instance,
cockroaches (Periplaneta americana) and houseflies had on average the same
time-to-peaks for light responses when dark adapted. When light adapted, the
housefly was significantly faster than all the other tested species. Moreover, other
fast flying species (dragonflies and drone flies) were found to be significantly
faster than species having a slower style of life (locusts and crickets).
Unfortunately the cockroaches were reported to be so unstable and difficult to
record from intracellularly that the light adapted responses were not published. It
thus seemed that all insects, even the fast flying diurnal species, have long
integration times or slow responses when dark adapted, but that it was only the
fast and agile animals that had significantly faster responses when light adapted.
The speeding up of phototransduction with increasing light adaptation has
since been confirmed with more elegant methods in several species. It occurs
even in the locust (Kuster & French 1985, Faivre & Juusola 2008) and in the
nocturnal bee Megalopta (Frederiksen et al. 2008), though with a lesser extent
than in fast flying species (Juusola et al. 1994, Juusola & Hardie 2001a). This
shows that the integration time of light responses in all these species is regulated
and adapts on the basis of ambient illumination conditions. This regulation
enables them all to see something in a dim environment. On the other hand, it also
increases the information capacity, i.e. the amount of information carried per unit
of time (in bits/s), of the photoreceptors in the bright conditions. The faster the
transduction (i.e. the wider the band width) the more information that can be
carried (see e.g. Shannon & Weaver 1949, Juusola & Hardie 2001a), given that
the SNR is either constant or increases with the intensity as well.
What about the behaviour of temporal summation with increasing light
adaptation in the cockroach? It is known that in dark adapted cockroach
photoreceptors the quantum bumps, as well as the impulse responses, are large
and slow (Smola 1976, Laughlin 1981, Howard et al. 1984), but very little is
known about how the bumps or their summation into receptor potentials behave
with light adaptation. It can be seen from the data presented by Butler and
Horridge (1973a) that the light-adapted impulse responses are at least to some
extent faster than the dark-adapted ones. Smola (1976) reported that the properties
of light-induced noise change with increasing light adaptation indicating changes
in the quantum bumps.
39
Spectral sensitivity
Already half a century ago it was reported (Walther 1958a) that Periplaneta had a
peak in spectral sensitivity at the wavelength of green light (507 nm) and that the
sensitivity also increased towards the violet end of the spectrum. It was concluded
that in the visual system of the cockroach there are two photoreceptor systems,
which differ in their spectral sensitivity. Later this was confirmed and more
exactly described (Mote & Goldsmith 1970, Mote & Goldsmith 1971) with
intracellular recording and staining in the white eyed mutants of the American
cockroach. In these studies it was shown that there are two spectral types of
photoreceptors. The UV receptors have a peak in sensitivity at 365 nm and their
sensitivity falls very fast at longer wavelengths. The green receptors have a
sensitivity peak at 500-510 nm and also some sensitivity in the UV range.
Through staining of the photoreceptors by intracellularly injecting them with two
dyes of different colours they were able to show that these two spectral receptor
types existed within the same ommatidium.
Based on the number of random recording electrode penetrations it has been
reported (Mote & Goldsmith 1970, Mote & Goldsmith 1971) that in the dorsal
part of the eye there were almost as many UV as green receptors (11:13). Then
again, on the same basis it has also been argued this same relation is 4:25
(Heimonen 1990). The location and number of spectrally different photoreceptors
has also been studied through microscopy (Butler 1971) by utilising selective
light adaptation with either green or UV light and palisade formation in the
photoreceptors due to light adaptation. On the basis of identifying the
photoreceptors anatomically (as explained in the previous chapter, 2.3.2) it was
reported that the relative number of UV to green receptors is 3:5 in each
ommatidium throughout the whole retina. Different spectral types always had the
same position in relation to each other in every ommatidium and the eyes were
mirror images of each other in this respect. In contrast to these findings, Ribi
(1977) hypothesized that all the green receptors have a synapse terminal in the
lamina and UV receptors in the medulla. Mote (1990) also claims in his review to
have shown this in his own (unpublished) studies. They also both strongly imply
that there is only one UV receptor, i.e. a photoreceptor with a terminal in the
medulla, per ommatidium. On the grounds of these controversies it can be
summarized that the relative number of green and UV receptors is still an open
question, as well as the functional or spectral identity of the photoreceptors
terminating in the medulla.
40
Receptive fields, photoreceptor sensitivity and adaptation
The acceptance angle (Δρ) of dark-adapted green receptors in Periplaneta has
been reported to be 6.7°±1.8° (mean ± SD) in the horizontal and 6.9°±1.3° in the
vertical plane (Butler & Horridge 1973a). In the light-adapted state the
corresponding values of Δρ were markedly narrower: 2.4°±0.9° and 2.3°±0,6°.
These changes in Δρ are explained by the clustering of pigment granules close to
the rhabdoms in the light-adapted state and the formation of a palisade during
dark adaptation (Snyder & Horridge 1972, Butler 1973b). By lowering the optical
density of the tissue around the rhabdom, the palisade increases the difference in
the refractive indices between the rhabdom and its surroundings. Since the
rhabdom acts as a light guide, this leads to incident light being accepted by the
rhabdom over a wider angle.
Butler & Horridge (1973b) have also studied the effect of light adaptation on
the sensitivity of cockroach green receptors. They recorded V-logI curves and on
the basis of the stimulus intensity values inducing half-maximal responses noticed
that even in the same state of adaptation the photoreceptors had roughly 10-fold
differences in their sensitivity. In addition, on average the dark-adapted cells were
about ten times more sensitive than the light-adapted ones. Disappearance of the
palisade in light adaptation (Butler 1971, Butler 1973b) explains only about 25%
of the drop in sensitivity (Snyder & Horridge 1972). The rest, 75%, is probably
explained by the clustering of the pigment granules around the rhabdom.
According to theoretical calculations, the pigments in these granules should be
about 70 times more efficient in absorbing light than the visual pigments, in order
to induce this sensitivity drop.
The green receptors of cockroaches have also been reported to be polarisation
sensitive (Butler & Horridge 1973b), i.e. to be about five times more sensitive to
light polarized in the optimal plane than to light polarized to the plane orthogonal
to this optimum. In addition, photoreceptors could be divided into two groups
having optimum planes of polarization 90° apart. These two optimum planes were
supposed to correspond with the two orientation planes of the rhabdomeric
microvilli. On the other hand, it has also been reported that only some (about 25%)
of the photoreceptors are polarisation sensitive and most of them are not
(Heimonen 1990).
41
Processing of light information in the optic lobes, CNS and ocelli
Very little is known about information processing in the optic ganglia of the
cockroach. There are no reports of any recordings or functional studies on the
lamina. And though there are some electrophysiological studies on cockroach
medulla (Mote et al. 1981, Mote & Rubin 1981, Kelly & Mote 1990b), the
findings are scattered and the general picture is very unclear (review by Mote
1990). Neurons in the medulla respond to light stimuli with action potentials,
some with ‘on’-responses and others with ‘off’-responses. Many neurons spiked
only when lights were on, others continuously, with only the spiking frequency
modulated by the light. On the other hand, neurons in the medulla were colour
sensitive and also had largely variable latencies and receptive fields. There are no
publications on the function of cockroach lobula.
Large calycal giant neurons terminating in the mushroom body of the cockroach
brain have been examined by intracellular recording and staining (Nishino &
Mizunami 1998). They exhibit spontaneous and rhythmic burst of spikes, which were
suppressed by olfactory, visual, tactile or air current stimulation. The mushroom
bodies of the cockroach are also shown to participate in place memory (Mizunami et
al. 1998) like that described in more detail earlier (in section 2.3.1). A few studies
have appeared on the electrophysiology of neurons responding to light stimuli in the
cockroach ventral nerve chord in the thorax (Cooter 1973, Edwards 1982a, Edwards
1982b). These neurons also reacted to light stimuli either by bursts of action
potentials or by changes in the frequency of continuous spiking. Cooter (1973) found
several different types of neurons responding to light stimuli, some only to ‘lights off’
and some only to ‘lights on’ stimuli, and others only to moving lights. A few of the
cells responded only, or also, to ocellar light stimulation and some also to air currents
directed toward the antennae. Edwards (1982a, 1982b) in turn did some studies on the
so-called descending contralateral movement detectors, which are neurons that react
only to movements of the light stimulus. He found that they respond both to UV and
green light, with no colour discrimination. Instead, the neurons segregated the
intensities of moving lights and adapted on the basis of background light intensity.
The anatomy, and even the electrophysiology, of Periplaneta ocellar neurons are
known to a much greater detail (reviewed by Mizunami 1994, Mizunami 1995a) than
the corresponding features in the compound eyes. Periplaneta has two white (nonpigmented) ocelli situated frontally on the head between the antennae. There are over
10000 photoreceptors in an adult ocellus (Toh & Yokohari 1988) and on the basis of
ERG recordings they are all reported to be green receptors (Goldsmith & Ruck 1958).
42
All the photoreceptors synapse onto only four second-order neurons, which for their
part terminate onto 15 third-order cells (Mizunami et al. 1982, Mizunami & Tateda
1986, Mizunami 1994, Mizunami 1995c, Mizunami 1995b). The 2nd and 3rd order
neurons have been described in detail not only in structure, but also in function
(Mizunami et al. 1986, Mizunami & Tateda 1988b, Mizunami & Tateda 1988a,
Mizunami 1990, Mizunami 1994, Mizunami 1996). They have connections to the
lamina, medulla and lobula of the compound eyes, to other sensory ganglia in the
head, motor ganglia and several areas in the brain. The light responses of the 2nd order
cells are membrane hyperpolarisations with off-spikes and those of 3rd order neurons
are depolarisations, hyperpolarisations, action potentials and their combinations.
Some of the 3rd and 4th order neurons in the ocellar tract are multimodal and react also
to stimuli given to other sensory systems, e.g. the mechanosensory system of the
antennae (Ohyama & Toh 1986, Mizunami 1995a). On the other hand, although there
is ample information on the structure and function of the ocellar neurons, very little
known about what the ocelli are used for in the cockroach (Mizunami 1996).
Spatial integration
In addition to slow responses and temporal summation during dark adaptation
(Smola 1976, Laughlin 1981, Howard et al. 1984, see also above), the cockroach
visual system seems to utilize the strategy of collecting light in space i.e. spatial
summation. The first method to spatially integrate low intensity light signals is to
have wide photoreceptor receptive fields in dim or dark conditions (Butler &
Horridge 1973a). This is accomplished by wide and large ommatidia, and
especially by wide rhabdoms (Trujillo-Cenoz & Melamed 1971, Wolken 1971,
Butler 1973b), with some additional structural features to insure maximal light
capture. On the other hand the receptive fields in dark conditions are even wider
because of pigment migration and palisade formation around the rhabdom (Butler
1971, Snyder & Horridge 1972, Butler 1973b, Ferrell & Reitcheck 1993).
The other strategy of spatial integration is the summation of response signals
from several photoreceptors to each of the 2nd order neurons. There are several
anatomical findings that suggest that a fair quantity of spatial pooling takes place
in the 1st synaptic area of the cockroach visual system. According to Ribi (1977)
photoreceptor axons regroup on their way to the lamina to form bundles of 6-20
axons. The postsynaptic arborisations of the 2nd order monopolar cells in the
lamina are also wide and large, as is typical of nocturnal species (Greiner et al.
2005, Greiner 2006, Warrant 2006, Warrant 2008). Ernst and Füller (1987) also
43
reported that regrouping takes place in the photoreceptor axons before lamina, but
they did not give any exact figures on the number of axons in the new bundles.
Instead, they measured that the surface area of distal lamina is less than 10% of
the area of retina. It has also been reported (Füller et al. 1989) that cell counts in
the lamina and medulla are far smaller (about the order of 2000-3000 per
ganglion) than the number of photoreceptors per eye (26000-45000). The exact
answer to what extent and how this pooling of visual information in cockroach
compound eyes occurs is still missing. In the cockroach ocelli it is known that
over 10000 photoreceptors are pooled to only four 2nd order neurons (Toh &
Yokohari 1988, Mizunami 1995c, Mizunami 1995b). In the medulla of the
compound eye the quantity of spatial summation is probably even larger than in
the lamina, since the receptive fields of medulla neurons have been reported to be
very wide (Mote et al. 1981). All this spatial summation of light information
naturally and inevitably leads to a decrease in spatial resolution.
44
3
Aims of the research
The general aim of this thesis has been to reveal the functional features by which
the photoreceptors of the American cockroach (Periplaneta americana) have
adapted to cope with dim light conditions. The American cockroach was chosen
to be the target of the study, since it was abundantly and easily available, there
was already some basic information on its visual system and it seems to be an
evolutionary success story. There are two things that made Periplaneta americana
exceptionally interesting and challenging to study. First of all, it prefers living in
dark or extremely dim environments, but has still retained large and complex
compound eyes in the course of evolution. Secondly, several peculiarities and
mysteries, e.g. structural irregularities, were known to exist in its visual system
without any proper explanation.
The more specified aims of the original articles upon which this thesis is
based on were:
I
To map the functional correlates of the known irregularities in the structure of
the cockroach compound eye, i.e. to quantify the possible variability in the
properties of light responses and to formulate a theory how this kind of visual
system could work.
II To properly describe the temporal summation properties or dynamics of the
light responses of cockroach photoreceptors at different levels of light
adaptation and to determine if they are matched to low levels of ambient
illumination.
III To determine and describe the light response properties in the long axons of
the cockroach photoreceptors.
45
4
Summary of the material and methods
4.1
Animals and preparation
In this thesis the green sensitive photoreceptors (Mote & Goldsmith 1970, Mote
& Goldsmith 1971) of the adult male cockroach (Periplaneta Americana; Fig. 1A)
compound eye have been studied. A few females were also tested to ensure that
the light responses and their variability did not differ significantly from males.
Cockroaches were maintained in a 24 h (12:12) light-dark cycle either at room
temperature (20-22 °C) or at 25-27 °C. Recordings were done during different
phases of the circadian rhythm and always at the room temperature.
For capturing, the cockroaches were anaesthetized with CO2. When the effect
of the anaesthesia started to fade out and the antennae and the legs started moving
again, the cockroach was dissected behind the front legs. The front legs and the
antennae were removed. To stop the interior of the head capsule from moving two
approximately 1 mm deep incisions were made over the face of the cockroach.
The vertical incision was made from top of the head between the compound eyes
along the facial mid line down close to the jaws. The horizontal incision was cut
below the compound eyes across the whole face. All the cut surfaces and wounds
were sealed with wax. The remaining front part of the animal was fixed on the
edge of a small glass plate with wax so that the upper part of the head and the
compound eyes ended on top. Two small incisions were made on the dorsal
corneal surface of the left compound eye so that a small sector of the cornea could
be removed. To prevent dehydration the formed opening was quickly covered
with an emulsion of white Vaseline and distilled water.
The cockroach preparation with the glass plate was placed on a custom made
cardan arm system (courtesy of Mikko Lempeä, University of Oulu, Department
of Physical Sciences; Fig. 1B) so that the left eye was placed in the centre of an
imaginary sphere along whose surface the stimulation light could be moved. With
the aid of a small xyz-micromanipulator (WPI, USA) sitting on top of the central
pole of the cardan arm system, and below the preparation, the grease-covered
opening in the eye was positioned so that the recording electrode could either be
directed to the photoreceptor somata in the retina or the axons below them. The
reference electrode (a chlorided silver wire) was placed through a small additional
incision in the tergum (or the cornea) into the thorax (or the right eye). The
recording electrode was inserted through the grease-covered opening into the
46
tissue of the left eye with a piezoelectric micromanipulator (PM 10, WPI, UK in I
and II; Inchworm PZ-550, Burleigh, USA in III). The experimenter controlled the
position of the head, the angle of the grease-covered opening in the eye and the
direction and the location of the recording electrode by looking through a
stereomicroscope (Leica, Germany in I and II; Nikon, Japan in III) equipped with
cold light source, which could be freely aimed in the desired direction.
A
B
C
Fig. 1. A. A male American cockroach. B. The custom made cardan arm system. C. The
whole experimental setup.
47
Most of the recordings were done from the area of the black retina i.e. the
somata of the photoreceptors. Only some of the impulse responses (those with
superimposed spikes) were recorded from the photoreceptor axons (I, III). After
the preparation and electrode positioning the photoreceptors were left to darkadapt for a period of about 20-30 min. Long periods of light stimulation were
always followed by dark-adaptation of at least several minutes before the next
stimulation run was started. The length and level of dark adaptation was
considered to be adequate when the impulse response had retained its original
amplitude.
4.2
Recording and stimulation equipment and procedures
The voltage responses of the photoreceptors to light and current stimulation were
recorded intracellularly with borosilicate glass capillary microelectrodes
(resistances 50-150 MΩ) pulled with a Flaming/Brown puller (P-87, Sutter
Instrument, USA) and usually filled with a 2 M KCl solution, whose pH was
adjusted to 6.84 with a potassium-phosphate buffer. When intracellular staining
and marking of the recorded cells (Stewart 1978, Stewart 1981) was desired (III),
the electrodes were filled with 3% Lucifer yellow CH (Sigma Co.) in 0.1% LiCl
(in this case electrode resistances were 150-250 MΩ). The electrode capacitances
were compensated and the recorded signals amplified with an intracellular
amplifier (SEC-1L or SEC-05L, NPI, Germany) equipped with a low voltage
probe.
The light stimuli were either pulses of given intensities and lengths, or
pseudorandom contrast noise sequences (French 1980b, French 1980a) with
desired intensity modulations at different mean adapting background light levels.
The current stimuli given through the recording electrode were either pulses of
given amplitudes and lengths or pseudorandom white noise sequences with
desired modulations and DC levels. In these recordings the amplifier was driven
in discontinuous current clamp (switched-clamp) mode. All the stimulus voltage
waveforms were originally produced, all the measurements controlled and the
signals analyzed with either an Asyst (Keithley, USA) or a Matlab (Mathworks,
USA) based custom made software (programming in Asyst by Eero Kouvalainen
and Mikko Juusola, Biosyst in Matlab by M. Juusola) running on a computer
equipped with a data acquisition card (Data Translation DT2821 or National
Instruments PCI-MIO-16E-4, USA). The light stimuli were produced either with
a green high intensity LED (HBG5666X, Stanley, Japan, peak wavelength 555
48
nm or B5-433-B525, Roithner Lasertechnik, Austria, 525 nm) or a stroboscope
(Cathodeon Ltd, UK), whose flash was guided to the cardan arm by a quartz cable.
The stroboscope was used to produce 10 μs white light flashes for eliciting
impulse responses. When necessary the intensity of the stroboscope flashes could
be attenuated by neutral density grey filters (Schott, Germany, transmission 49, 14,
2.5 or 0.09%). The LED was used when either longer light pulses or light
intensity modulation with a background (e.g. contrast noise) was desired. The
computer generated stimulus waveforms for the LED were fed to a custom made
voltage-to-current driver (courtesy of Eero Kouvalainen), which controlled the
current and thus the intensity modulation of the LED. The driver was equipped
with a voltage output monitoring the LED current. When necessary, additional
light intensity control was achieved by placing grey filters (Kodak Wratten, USA,
attenuation 0.5-5.0 log units in steps of 0.5) between the stimulating LED and the
eye. The monitored stimuli and the amplified response signals were low-pass
filtered with a matched two-channel filter (VBF/23 or Benchmaster VBF8, Kemo,
USA). A photograph of the experimental setup is shown in Fig. 1C.
When it was necessary to know the stimulus light intensity in relation to an
individual photoreceptor cell and its sensitivity (I, II), i.e. while recording for
example light adapted step responses, SNR, transfer and coherence functions, the
light output of the LED was calibrated by counting the number of single photon
responses (see e.g. Lillywhite 1977) during a prolonged dim illumination. The
mean brightness of all the adapting background light levels used in the recordings
was then calculated according to this calibration and the optical density of the
grey filters (0 – 5.0 in steps of 0.5) used in each case. Values of absorbed photons
per second (ph/s) obtained this way are so-called effective values (Juusola et al.
1994, Juusola et al. 1995) and have to be taken as measures of cell specific
relative intensities only. They are not absolute values, i.e. absolute measures of
absorbed photons (except at the calibration light level itself), since the quantum
efficiency of the photoreceptors changes with the state of light adaptation (Smola
1976, Wu & Pak 1978, Laughlin 1981). However, the obtained stimulus intensity
scaling is related both to the brightness of the stimulating light and to the
sensitivity of the photoreceptor stimulated. In addition, when recording from
light adapted photoreceptors, the dark adapted cells were allowed to adapt for 90120 seconds to the adapting background i.e. mean illumination before introducing
light contrast stimuli (steps or noise). This was done to ensure that the sensitivity
of the photoreceptors had reached a steady-state and that most forms of adaptation
were completed, including possible palisade formation and pigment migration
49
(see e.g. Butler 1973b). The light contrast (c) was defined as a change in the light
intensity (ΔI) divided by the mean light background (I):
c=
ΔI
.
I
In the case of pseudorandom contrast modulation, the standard deviation (SD) of
the stimulus was taken as ΔI (cf. Kouvalainen et al. 1994). When many different
adapting background intensities were applied successively to a cell the
experiments were first performed at lowest adapting backgrounds before
proceeding to higher ones. In the beginning, or before frequency domain analysis,
the offsets and the possible trends were always removed from the signals.
4.3
Quality of recordings
The requirements of a photoreceptor soma to be accepted for the recordings were:
a sufficiently low resting potential (about -55 to -65 mV), a high input resistance
(60-100 MΩ, when estimated from a response to hyperpolarizing 0.5 nA current
pulse given at resting potential), and clearly visible quantum bumps at a low
stimulus light level (amplitude several millivolts, cf. Fig. 1 in I). In the axon
recordings only the resting potential criterion was applied. When a cell recording
fulfilled these requirements, the cardan arm was moved so that the maximal
amplitude for the light responses was found. All the recordings were done along
this optical axis. A recording was rejected, if the sensitivity and time-courses of
step responses did not return to their initial values or the above-mentioned quality
criteria were not also satisfied after the recording.
4.4
Marking and anatomical identification of cells with spike
responses
Some of the cells (n = 4), where impulse responses were combinations of graded
responses and spike or action potential like responses, were marked and
anatomically identified (III) with intracellular Lucifer yellow dye injections
(Stewart 1978, Stewart 1981). In these experiments the recording electrodes were
filled with 3% Lucifer yellow CH (Sigma Co.) in 0.1% LiCl and the recorded
cells were marked with iontophoretic injections of the dye. Iontophoresis was
made with either short 1-2 nA pulses or a constant 1 nA current and lasted all
50
together about 15 minutes. To avoid extracellular dye injections, the marked cells
were tested to have similar impulse responses both before and after the dye
injections. Immediately after iontophoresis the electrode was cut along the surface
of the eye, the eye surgically removed from the head and fixed overnight with
10% formaldehyde in buffered insect saline. Next morning the fixed eye was
embedded in Historesin (Cambridge Intruments, UK), cut into 30-80 μm slices,
examined by fluorescence light microscopy (Zeiss, Germany) and photographed.
When Lucifer yellow stained cell parts appeared in several slices, a camera lucida
drawing combining these slices and cell parts was done by hand (Fig. 2B in III).
4.5
Localization of recorded photoreceptor responses
Electrode locations in the cockroach eyes were roughly estimated by utilizing the
natural colour differences in the tissue. The part of the retina containing
photoreceptor somata and pigment cells is black due to screening pigment
granules and photopigments (see also Butler 1973a, Butler 1973b). In contrast,
the area below the retina containing distal parts of the photoreceptor axons is
brown due to pigment granules in the beginning of the axons and the rest of the
axonal area up to the lamina is opalescent or very light brown, depending on the
quality of incident light. These colour differences in the tissue always correlated
with different impulse responses. The pure graded responses were recorded only
from the photoreceptor somata on the area of black retina and the graded
responses with superimposed spikes from the opalescent axon area (Fig. 2, cf.
also Fig. 6 in I). In the brown area below the retina or quite proximal retina the
graded responses had small spikelets on top of them.
4.6
Recordings of photoreceptor impulse and step responses in
dark adaptation
Responses to very short (10 μs), bright white light flashes, i.e. impulse responses,
were recorded from both photoreceptor somata and axons (I, III). All the cells
were dark-adapted before the recordings. Because the inter-stimulus interval (ISI)
used in these experiments was only 1 s, the more sensitive and slowly adapting
photoreceptors (see II) were probably partly light-adapted during these recordings.
Since there are spikes or spike-like potentials in the axons (I, III), these
recordings were sampled at 2.5 kHz and not filtered.
51
Fig. 2. A schematic drawing of the cockroach eye with estimated locations of
recording electrodes and different impulse responses. The figure is based on a
camera lucida drawing with two intracellularly stained (here black) spiking
photoreceptors (cf. Fig. 2B in III); Re = retina, Bm1 and BM2 are the two basement
membranes of cockroach eye, La = lamina, Me = medulla. On the right is a micrograph
of an unstained thick slice (ca. 80 μm) of fresh tissue taken with standard transillumination. Its rough location in the eye tissue is also shown. The colour differences
in the tissue enable approximate localisation of the recording electrode and different
responses (see text). Three sets of recorded light responses from different locations
are shown above the drawing. Their estimated locations are marked with the
microelectrode-like arrows. Each set includes impulse responses to five flashes of
white light (10 μs) with different relative intensities (100; 49; 14; 2,5 and 0.09%) given
at the beginning of the traces. Above the topmost set an insert shows a response with
two superimposed spikes.
The stimuli used while recording the sensitivity and speed of adaptation in
photoreceptors (I, II) were either 300 ms or 10 s light pulses of desired intensities
with long enough ISIs (usually several seconds and with longer stimuli even
minutes) for preserving a proper dark adaptation. These recordings were done
only in photoreceptor somata, low-pass filtered at 700 Hz and sampled with 2
kHz. The intensity of the stimulus producing a half-maximal light response was
used as a measure of sensitivity and the amplitude of depolarization at certain
52
time points after stimulus onset (300 ms and 9.9 s) in relation to the amplitude of
the maximum response (A/Amax) during a bright (saturating) light pulse as a
measure of adaptation speed (cf. Fig 3 in I).
4.7
Recordings of photoreceptor step responses in light
adaptation
When recording step responses of light adapted photoreceptors (II), the LED
output was first calibrated in each cell at a low constant intensity by counting the
quantum bumps produced as a response. Together with the neutral density filters
used this produced a cell-dependent intensity scale (as explained earlier in section
4.2). After this the cells were light adapted with the desired constant background
intensity for 90 s and then 300 ms contrast pulses were presented on top of each
background with 1 s ISIs. As before, the recording procedure advanced from the
dimmest to the brightest stimuli. The contrasts used in pulses at each background
were from -1 to +1 in steps of 0.2 contrast units. These recordings were filtered at
700 Hz, sampled with 2 kHz and averaged 10-20 times.
4.8
Determination of receptive field
The recordings of receptive fields were done (I) with a different setup (courtesy
of E. Warrant in the University of Lund, Sweden), described in detail elsewhere
(Warrant et al. 2004). In short, the setup was similar to that used in other
recordings, but the light source was a Xenon arc lamp equipped with a quartz
light guide and the cardan arm system was better suited for accurate receptive
field measurements. The operating curves (amplitude of voltage responses vs.
stimulus intensity i.e. functional sensitivity) and the horizontal receptive fields
(i.e. angular sensitivity) of the dark-adapted photoreceptors were measured with
impulse responses sampled at 2.5 kHz and at one degree intervals. The ISI was
always long enough to maintain dark adaptation. The acceptance angles (Δρ; the
width of the receptive field at 50% sensitivity level) were determined from these
sensitivity functions with linear interpolations between the recorded data points.
4.9
Determination of signal-to-noise ratio
Photoreceptor SNR in the frequency domain was recorded and calculated (I) as
described in detail by Kouvalainen et al. (1994, see also Juusola et al. 1994). In
53
short, when SNR was to be recorded, each cell was first individually calibrated in
terms of absolute sensitivity by counting the quantum bumps produced at a very
low constant illumination (as explained in section 4.2). Before starting a SNR
recording session at a certain mean adapting light intensity, the cell was lightadapted for 2 min, to allow the photoreceptor to reach a steady state
depolarization. During recording, a pseudorandom contrast noise sequence was
repeated 20-30 times on top of each mean adapting light level. Each recorded
sequence was 16 s long and the stimulus had a contrast of 0.16 or 0.32. Both the
stimulus and the response were filtered at 500 Hz and sampled with 1 kHz. The
time domain average of the responses gives an estimate of the signal, and any of
the raw responses minus the signal is considered noise. The power spectra of the
signal and the noise were calculated with an FFT algorithm from 2048 point
segments (ca. 2 s) with 50% overlaps using a Blackman-Harris fourth term
window (Harris 1978). The SNR in the frequency domain is the quotient of these
spectra.
4.10 Determination of frequency response
Photoreceptor frequency responses i.e. transfer functions (gain and phase) were
recorded and calculated (II) as described in detail by Juusola et al. (1994). In each
cell, where the transfer function was recorded, a coherence function for the same
responses was also calculated. Again, as in the case of the SNR, each cell was
first individually calibrated in terms of absolute sensitivity by counting the
quantum bumps produced at a very low constant illumination (described in detail
in section 4.2). Then all other background intensities (in effective ph/s) were
calculated according to this biological calibration and the optical density of the
grey filters used in each case. Before starting a transfer function recording session
at a certain background light intensity each cell was light-adapted for 90 s, to
allow the photoreceptor to reach a steady state depolarization. Then a
pseudorandom contrast noise sequence (French 1980b, French 1980a) was
repeated 10-20 times on top of each adapting background light level. The
recorded sequences were 16 s long and the stimulus had a contrast of 0.32. Both
the stimulus and the response sequences were filtered at 500 Hz, sampled at
1 kHz and averaged in the time domain (as with SNR). The power spectra of the
stimulus and the response and the cross power spectrum were calculated from this
time domain averaged data with the same method as described earlier in section
4.9 (i.e. FFT, 2048 point segments, 50% overlap, a Blackman-Harris window).
54
The transfer function is the quotient of the cross power spectrum and the stimulus
power spectrum. The coherence function is obtained by multiplying the cross
power spectrum with its complex conjugate and dividing the result with the the
multiple of the power spectra of the stimulus and the response (see e.g.
Marmarelis & Marmarelis 1978, Juusola et al. 1994). From the transfer functions
(gain and phase) the linear impulse responses (or 1st order Wiener kernels) could
be calculated via the inverse FFT.
4.11 Evaluation of quantum bump size and shape in light adaptation
Before starting a SNR recording or pseudorandom contrast modulation (see
section 4.9) a 16 second sequence of voltage response (or photon noise) to
constant background light was always recorded at all the different background
intensities. The power spectrum of this photon noise was calculated as described
earlier (sections 4.9 and 4.10). When the phototransduction system was estimated
to be a minimum phase system (cf. Wong & Knight 1980, Burton 2006) , it
allowed the phase function to be calculated on the basis of the power spectrum via
the Hilbert transformation. Combining the measured power spectrum and
calculated phase function estimate enabled (via inverse FFT) the calculation of
the average shape of the events creating the spectrum, i.e. quantum bumps (II).
4.12 Determination of impedance function
The impedance function here means a transfer function or frequency response
between the injected (via the recording electrode) pseudorandomly modulated
current noise stimulus and the measured membrane voltage response. This
frequency response was recorded and analyzed (II) exactly as the frequency
response earlier (section 4.10), only that the stimulus was current (not light) noise
with a SD of 0.16 nA. A more thorough explanation of measuring the cell
impedance with white noise modulated current injection is given by Weckström et
al. (1992, see also Juusola & Weckström 1993).
4.13 Statistical tests
To test the normality of the distributions of the variability in light responses (I) we
used both the Kolmogorov-Smirnov and the D’Agostino-Pearson test in MedCalc
55
(MedCalc Software, Belgium) and the Lilliefors test in Matlab (Mathworks,
USA).
4.14 Modelling and simulations
Modelling of the cable properties of cockroach photoreceptors to test the
propagation and attenuation of light responses (III) was done in the frequency
domain according to the algorithm developed by van Hateren (1986), originally
for fly photoreceptors and visual interneurons (van Hateren 1986, van Hateren &
Laughlin 1990). The model was programmed with Mathcad (MathSoft Inc, USA)
and in it the photoreceptor was modelled as a series of isopotential membrane
cylinders of different sizes with connecting transfer impedances. The sizes of
different cell parts were estimated on the basis of the anatomical information
available on cockroach photoreceptors (Butler 1973a, Ribi 1977, Ernst & Füller
1987). In the model the current (simulating the light-induced current) was injected
into the middle of the soma cylinder and the voltage transfer between the soma
and the axon terminal could then be obtained.
The hypothesis concerning how a group of variable photoreceptors might
work as a population was simulated with a Simulink (Mathworks, USA) program
(I). A general description of this simulation is given in the summary of the results
(section 5.8), and the details are given in the original paper (I) and its
supplementary material. The simulation files are available from the authors via
email ([email protected] or [email protected]) upon request.
56
5
Summary of the results in the original
articles
In this summary the visual information processing in cockroach photoreceptors is
approached from the view point of filtering the light response signals. Both in
order to have a general view of the processing of light information and to dissect
it into smaller pieces, this has proven to be a very fruitful and thus widely used
approach also in other species, e.g. horseshoe crab Limulus (Dodge et al. 1968,
Wong & Knight 1980, Wong et al. 1980, Wong et al. 1982), blowfly Calliphora
(French & Järvilehto 1978, Weckström et al. 1988, Weckström et al. 1992,
Juusola & Weckström 1993, Juusola et al. 1994, Juusola et al. 1995), fruitfly
Drophila (Juusola & Hardie 2001a, Juusola & Hardie 2001b, Niven et al. 2003,
Vähäsöyrinki et al. 2006), other Dipteran flies (French 1979, French 1980b,
French 1980c, Burton et al. 2001, Burton 2006), locusts (Kuster & French 1985,
Faivre & Juusola 2008), and the bee Megalopta (Frederiksen et al. 2008, Warrant
2008). Traditionally in this approach visual systems or photoreceptors are
considered to be formed of a series of filters that shape the response signals and
their information content, while the signals propagate along the photoreceptors or
in broader sense the visual pathway. Hence, the cockroach photoreceptor together
with the preceding optics can be seen as a series of filters depicted schematically
in Fig. 3.
All together hundreds of adult male cockroaches were used in the
experiments of this thesis. About 20 adult females were also tested and found to
have similar behaviour of light responses. Intracellular recordings were made
from hundreds of photoreceptors and of those around 300 cells had high enough
quality, at least in some of the recorded signals, to be accepted among the results
of the articles (I, II, III) and the thesis. In sections 5.1-5.6 only the properties of
light responses recorded from the somata of the photoreceptors are considered,
and the responses of the axons are covered in sections 5.7 and 5.8. The spectral
filter of the cockroach photoreceptor (see Mote & Goldsmith 1970, Mote &
Goldsmith 1971, Heimonen 1990) was not studied in the original articles of this
thesis. Hence, it is not presented in this summary, but it is reviewed in the
discussion part of this thesis (section 6.1). Also, the ventral, narrowing part of the
eye was not studied, since all the recordings were made from either the large
dorsal area or the middle of the compound eye. It is unlikely, although possible,
that some properties, or at least their distributions, could be different in the
ventral part of the eye.
57
Fig. 3. General scheme of filtering in cockroach photoreceptors. The incident light is
filtered spatially and spectrally by the receptive field and the spectral sensitivity of the
photoreceptor. The effects of these two filters on coding can be bypassed when using
green light given along the optical axis. The next filter is phototranduction that
produces quantum bumps, which are summed up to form receptor potentials. All the
ionic currents are also filtered by the membrane of the photoreceptor. The membrane
filter alone can be studied by injecting currents into the photoreceptor and recording
the response voltages. The collective filtering properties of transduction and the
membrane can be studied by recording the frequency response of the cell. Coherence
and SNR functions between the light stimuli and voltage responses can also be
determined. The additional filtering effects of the axon membrane can also be
recorded. Light adaptation is known to have direct effects on at least the spatial filter
and the transduction. Direct adaptational effects on the membrane filters are not
known, but indirectly the changes in membrane voltage modify them.
5.1
The spatial filter
The angular sensitivity of photoreceptors forming the spatial filter was
determined under dark-adapted conditions from 41 green receptors (I). The
angular sensitivity showed considerable variability. The values of Δρ varied from
2.2° to 12.7°, the average being 6.4° (SD was ±2.7°) (Fig. 4; see also Fig. 4 in I).
These values, like many other variable properties in the cockroach eye, were
shown to be normally distributed. There was no apparent correlation of the
angular sensitivity with the time-course dynamics of the light responses.
As described in the review of literature (section 2.3.3) the spatial filter
narrows with the increasing light adaptation (Butler & Horridge 1973a). It is
neither known nor studied here to what extent, if at all, the variability of the
acceptance angle is retained in light adaptation.
58
1,0
Sensitivity
0,8
0,6
0,4
0,2
0,0
-12
-9
-6
-3
0
Angle (°)
3
6
9
12
Fig. 4. The spatial filter (i.e. the size of the receptive field) showed an astonishing
amount of variability between cells. Data points and fitted Gaussian curves show the
widest (red, squares, Δρ = 12.7°) and the narrowest (blue, triangles, Δρ = 2.2°)
horizontal dark adapted receptive fields. The average acceptance angle (Δρ) is 6.4°.
The average Gaussian is shown as a dashed curve.
5.2
Properties of responses in dark and light adaptation and their
variability
Before the filtering approach to cockroach photoreceptors can be continued, we
need to take a more detailed look at the temporal properties of the light responses,
and especially on their large variability (cf. I and II).
Like other insect photoreceptors studied, stimulation by pulses of light under
otherwise dark-adapted conditions produced depolarizing voltage responses in
cockroach photoreceptors. They had relatively large (up to 70 mV) peak
amplitudes at the onset and a relaxation (or repolarisation) towards less
depolarized potentials, when longer stimulus pulses were applied. Primarily in
paper I, but also partly in II, it was shown that the variable properties of the
responses included, in addition to the angular sensitivity (see the previous section),
variations in absolute and relative sensitivity, adaptation speed, and signal-tonoise ratio. As with the angular sensitivity, the speed of adaptation was also found
to be normally distributed.
The variability of the absolute sensitivity was not very large (about 10-fold),
but in terms of the V-logI curve, it spanned over two decades, i.e. at least 100-fold
differences were found (see also Heimonen 1990). In particular, the speed of
59
adaptation, that is the speed of repolarisation of constantly illuminated
photoreceptors, had very large variation. For categorising purposes the cells were
described with terms like “non-adapting”, “adapting” and “hyper-adapting” (Fig.
5), although the variation in relaxation speed was shown to be continuous and
normally distributed both 400 ms and 9.9 s after the light onset (see Fig. 3 in I).
20 mV
non-adapting
2s
20 mV
200 ms
adapting
hyper-adapting
Fig. 5. Intracellular responses of different photoreceptors to saturating light pulses
enable the cells to be categorised into three classes (see responses to 300 ms pulses
on right) according to the variable time courses of their responses, although this
variation actually forms a continuum. “Non-adapting” cells adapt very slowly (or in
shorter timescale not at all) and “hyper-adapting” cells repolarise during illumination
close to the dark resting potential. The responses to 10 s light pulses (on left) have
been deliberately chosen to be similar in shape, but they repolarise to different levels
of membrane voltage at constant illumination. Significantly different shapes of
responses also exist (see Fig. 3A in I).
The responses to light pulses (in I and II) show prominent and variable
adaptation (in terms of repolarisation during the light stimulus) as described. This
behaviour was also investigated with pulses of light (contrasts between -1 and +1)
under light-adapted conditions in order to quantify the contrast coding properties
of the photoreceptors (II). As expected, the hyperpolarizing responses (with
negative contrasts) tended to be larger than the positive ones (with positive
contrasts) (Fig. 6A). To what extent this happened, and with which background
light intensities, also depended on the adaptive type of the response or receptor.
As in other insect species studied (e.g. Juusola 1993a, Juusola 1993b), cockroach
photoreceptors show clear improvement in their contrast coding abilities at
increasing background light intensities, when measured in terms of the maximum
amplitude of responses to contrast pulses (Figs. 6A and 6B). However, the most
noticeable finding was that this improvement saturated already with relatively low
background intensities, which in our experiments were usually for effective
60
intensities of 1000-10000 photons/s. The “hyper-adapting” cells, as in many other
respects, formed an exception. Their responses to all contrasts between -1 and +1
were always almost non-existent (Fig. 6C). The cells categorised as “nonadapting” seemed to code the negative contrasts with a higher gain than the
positive ones.
-40
"adapting"
320000 ph/s
13 mV
-44
mV
-48
12 mV
32000 ph/s
-52
14 mV
3200 ph/s
-56
11 mV
1000 ph/s
10 mV
100 ms
5 mV
320 ph/s
4 mV
100 ph/s
-60
B
10
100
1000
10000
100000
Intensity (ph/s)
"non-adapting"
2800 ph/s
15 mV
10 mV
100 ms
A
C
3000 ph/s
0 mV
"hyper-adapting"
Fig. 6. Light response properties of light adapted photoreceptors show capacity for
contrast coding even at low light intensities and also variation in their coding abilities.
The contrasts (ΔI/I) used varied between -1 and 1, and the adjacent contrast steps
were always 0.2 units apart from each other. All traces are averages of 10-20
recordings. A. Contrast response series at different backgrounds for an “adapting”
cell. The background intensities are presented on the right and the corresponding
“steady-state” depolarisations (in relation to the resting potential) on the left. The
stimulus waveforms are presented at the bottom. B. V-logI curves for the responses in
A. The maximum amplitudes at the same background light are connected with the
solid lines and the “steady-state” voltages in different backgrounds with the dotted
line. Notice the saturation of the “steady-state” voltage already at fairly low levels of
background light intensity (around 1000 ph/s). C. Examples of a contrast response
series for different cells (background intensities on the right and “steady-state”
depolarisations on the left). These responses depict variation in contrast coding
abilities between cells. The “non-adapting” cell only properly coded the negative
contrasts and the “hyper-adapting” cell did not code any of the applied contrasts.
61
5.3
The SNR of contrast coding
When light-adapted, the SNR of photoreceptors of studied diurnal (e.g. Juusola et
al. 1994, Juusola & Hardie 2001a, Faivre & Juusola 2008) and also nocturnal
(Frederiksen et al. 2008) insect species improves considerably, although it is
necessarily poor at very low light levels. Already with time-domain experiments it
was clear that the cockroach SNR is fairly low at any steady-state stimulation (see
previous section). When the SNR was determined with white-noise modulated
light stimulation (I) by using the averaging method of Kouvalainen et al. (1994)
to separate the signal and noise, it was found that in the cockroach the SNR also
improves upon light adaptation (Fig. 7, see also Fig. 5 in I). However, this
improvement was very limited, and the SNR always remained (at all background
light intensities covered) below 1.0 in the whole frequency band, typically in the
range of 0.1-0.3 (Fig. 7A). In addition, in most of the cells the improvement was
even smaller and the maximum SNR attained even lower (Fig. 7B). This shows
the very limited ability of cockroach photoreceptors to cope with increasing light
levels, and may be related to the saturation in the improvement in contrast coding
abilities that was depicted in the previous section.
0,4
SNR
0,4
0,3
"adapting"
0,3
0,2
0,2
0,1
0,1
0,0
0,0
A
1
10
Frequency (Hz)
100
SNR
B
"hyper-adapting"
1
10
100
Frequency (Hz)
Fig. 7. The SNR of contrast coding in different cockroach photoreceptors shows very
limited ability to cope with increasing illumination. A. The SNR of an “adapting” cell at
four different average light intensities: 100 (green), 1000 (blue), 32000 (red) and 100000
ph/s (black). “Non-adapting” (or very slowly adapting; cf. Fig. 5) cells behave similarly.
B. A “hyper-adapting” cell shows very low values of SNR at all background intensities:
44 (green), 440 (blue), 4400 (red) and 14000 ph/s (black).
The poor SNR and its lack of significant increase upon light adaptation also
show that the amount of information transmitted via a single photoreceptor in the
62
cockroach is very small. If the Shannon formula (e.g. Shannon & Weaver 1949)
for channel capacity is applied (cf. e.g. de Ruyter van Steveninck & Laughlin
1996, Juusola & Hardie 2001a, Niven et al. 2007, Faivre & Juusola 2008,
Frederiksen et al. 2008), with the very small bandwidth (ca. 15 Hz, see Fig. 5 in I)
and low SNR function values (maximum about 0.3-0.4 at the highest background
intensities used), we can estimate the channel capacity in the best case in bright
light to be only about 70-90 bits/s. This is about one order of magnitude smaller
than obtained with fast flying diurnal insects like Calliphora (Niven et al. 2007).
It is also several times smaller than in other flies like Drosophila (Juusola &
Hardie 2001a), and also remarkably more than twice as low as in the nocturnal
bee Megalopta (Frederiksen et al. 2008). This is relevant only on the assumption
that the system is linear and at steady-state, as under the experimental conditions
in the present work. Generally, it can be seen from the behaviour of both the SNR
functions (Fig. 7) and the coherence functions (Fig. 8) that the information
capacity of cockroach photoreceptors increases with increasing ambient
illumination, at least to some extent. However, since the values of coherence and
SNR were generally very low, the estimates of the information capacity can only
be suggestive; the exact information capacity would thus have a very wide
confidence interval (Bendat & Piersol 1971). Hence, they were not calculated to
any greater extent than this.
5.4
The frequency response
The dynamic linear properties of cockroach photoreceptors were estimated by
determining the frequency responses, i.e. the transfer functions (gain and phase),
and the coherence functions at different background intensities, using white-noise
modulated light stimulation and time-domain averaging (II). The frequency
responses showed a low corner frequency (around 20 Hz) and only very limited
improvement of contrast gain at increasing light intensities, consistent with the
experiments on contrast coding in light adaptation (cf. section 5.2). Most
noticeably, there was little or no increase at all in the corner frequency with
increasing light adaptation (Fig. 8A, see also Fig. 3 in II), in contrast to well
studied diurnal (e.g. Juusola et al. 1994, Juusola & Hardie 2001a, Faivre &
Juusola 2008) and nocturnal (Frederiksen et al. 2008) insect species. More
interestingly, in the cockroach there was more variation in corner frequencies
between different cells (15-25 Hz) than there ever was in a single photoreceptor
between intensities, where the maximal variation encountered was 19-24 Hz.
63
Hence, it made no sense to calculate average corner frequencies or to show them
as a function of intensity. Any improvements in the gain function at low
frequencies were limited to light intensities between 10-1000 photons/s, above
which the contrast gain was nearly constant. Consistent with the SNR
determinations (Fig. 7), the values of the coherence function remained small (Figs.
8B and 8C) even after extensive averaging (10-20 times). This was especially true
with the cells having a low level of “steady-state” depolarisation (< 5 mV) during
prolonged light stimulation (Fig. 8C). In cells that were able to maintain a higher
depolarisation level (5-15 mV), coherence values rose with increasing light
intensity (Fig. 8B), and again behaved similarly to the SNR values. The higher
values of coherence enabled the transfer functions to be evaluated fairly reliably
in these cases (Fig. 8A). In all cells the phase part of the frequency response
showed no changes at all, if we ignore the lowest intensities used (< 100
photons/s). At low intensities (< 400 ph/s) the coherence function always had
values near zero and the frequency response estimates were subsequently very
unreliable.
5.5
The transduction filter
To further evaluate the dynamic properties of phototransduction, the power
spectra of membrane noise, induced by constant background lights of different
intensity, were calculated (II). In the cells with notable levels of “steady-state”
depolarisation (5-15 mV) differences in the power spectra associated with the
increasing light adaptation could be found after prolonged (several minutes) light
adaptation (Fig. 9A). However, these differences were also present only at low
light levels, below an effective intensity of about 1000 photons/s. Above this
intensity the spectra of membrane noise induced by steady light remained
unchanged irrespective of light intensity. The shape and size of the events, i.e.
mainly quantum bumps, producing this noise could also be evaluated, and of
course these time-domain equivalents of the power spectra reproduced the results
(Fig. 9B). The bump amplitude and duration seems to decrease only up to an
intensity of about 1000 photons/s and then stabilises. These changes in bumps are
again consistent with the findings that the contrast coding saturated at about the
same intensities (1000-10000 ph/s, see Fig. 6 in section 5.2), as did the contrast
gain of the photoreceptors (>1000 ph/s, see Fig. 8).
64
Coherence
Gain (mV/unit contrast)
1,0
10
0,8
"adapting"
0,6
1
0,4
"adapting"
0,1
0,2
1
0
10
100
0,0
Frequency (Hz)
1
-300
B
-600
Coherence
-900
-1200
A Phase (degrees)
10
100
Frequency (Hz)
1,0
0,8
0,6
"hyper-adapting"
0,4
0,2
0,0
C
1
10
100
Frequency (Hz)
Fig. 8. Linear dynamics of photoreceptor light responses. A. Frequency response
functions (gain and phase) for an “adapting” cell at six different average background
intensities: 100, 320, 1000, 10000, 32000 and 100000 ph/s. The function with lowest
values (green) represents the lowest background (100 ph/s) and is the only one
marginally different from the others. This deviant behaviour may only exist because of
the unreliability of the estimate, caused by the low values of coherence (cf. B).
Although the contrast gain at low frequencies increases to some extent with
increasing background intensity, the phase functions are all alike. The corner
frequency of the contrast gain also shows this same temporally constant response
behaviour; it is nearly invariant at about 20 Hz. B. Coherence functions for the cell in A
at the same six levels of light adaptation: 100 (green), 320 (magenta), 1000 (cyan),
10000 (blue), 32000 (red) and 100000 ph/s (black). The values of coherence increase
with increasing light adaptation. C. Coherence functions for a cell that always had low
coherence values, i.e. for a “hyper-adapting” cell, at different levels of light adaptation:
14 (green), 140 (magenta), 440 (cyan), 1400 (blue), 4400 (red) and 14000 ph/s (black).
Note that the order of traces does not systematically follow the order of increasing
intensities. This even further implies that the cells with low “steady-state”
depolarisation levels fail to light adapt. The frequency response estimates of these
“hyper-adapting” cells would have been unreliable because of the always low
coherence values, and thus they were not calculated.
65
2
Power density (mV /Hz)
1
10
Relative voltage
.
0,2
0
10
"adapting"
-1
10
0,1
-2
10
-3
0,0
10
A
1
10
100
Frequency (Hz)
B
0
100
200
Time (ms)
2
Power density (mV /Hz)
Relative voltage
1
10
0,2
0
10
"hyper-adapting"
-1
10
0,1
-2
10
-3
0,0
10
C
1
10
Frequency (Hz)
100
D
0
100
200
Time (ms)
Fig. 9. Transduction filter, i.e. the relationship between the background or adapting
light intensity and the properties of quantum bumps or photon noise induced by
constant illumination. In different photoreceptors, the power spectra of photon noise
induced by constant background light demonstrated different quantum bump
behaviour as a function of light intensity. A. In the “adapting” cells there were some
changes in the power spectra at the lowest background intensities: 10 (blue), 100
(blue) and 1000 ph/s (red). The brighter backgrounds (10000 and 100000 ph/s; black)
led to similar photon noise and spectra as 1000 ph/s without any noticeable changes
with increasing intensity of illumination. B. Estimates of average quantum bumps for
the “adapting” cell in A at four different light intensities: 10 (green), 100 (blue), 1000
(red) and 100000 ph/s (black). The estimates are calculated on the basis of the photon
noise spectra in A and drawn with the corresponding colours. Notice that between the
intensities of 1000 and 100000 ph/s, the bump estimates are almost identical. C. In
“hyper-adapting” cells the power spectra were all alike in spite of the background
intensity: here 44 (green), 440, 1400, 4400 and 14000 ph/s. The “steady-state”
depolarisation of these cells was at or slightly above (here 1 mV) the resting potential,
irrespective of the light intensity. D. Estimates of average quantum bumps for the
“hyper-adapting” cell in C at two different light intensities: 44 (green) and 14000 ph/s.
The estimates are calculated on the basis of the photon noise spectra in C, and just as
the spectra, they are very similar.
66
The “hyper-adapting” cells, i.e. cells that had repolarised to the level of
resting potential during the light adaptation period before the recordings, didn’t
show any changes in the power spectra of constant-light-induced noise as a
function of increasing light intensity (Figs. 9C and 9D). This is probably simply
due to the fact that there seems to be almost no light response remaining in these
cells after the preceding light adaptation period, irrespective of the intensity; only
some sporadic discrete events resembling single quantum bumps (see Fig. 3A in I
or Fig. 1B in II).
5.6
The membrane filter
The properties of the non-transductive part of the cell membrane were
investigated with injections of current (II). Both current pulses and white noise
modulated current were used. Hyperpolarising current pulse injections yielded
estimates for the input resistances of the photoreceptors in the range of 60-150
MΩ, with time constants in the range of 20-50 ms (Fig. 10A). Whereas
hyperpolarizing current pulses induced more or less exponential charging of the
cell membrane in the dark, the depolarizing pulses showed clear rectifying
behaviour of the membrane voltage, indicating the presence of a strong voltagedependent K+ conductance, as in all other studied insect species (e.g. Weckström
et al. 1991, Laughlin & Weckström 1993, Weckström & Laughlin 1995).
The determination of membrane impedance mirrors the results obtained with
current pulses. In the dark and below the resting potential the impedance was
large (100±20 MΩ) for frequencies below about 10 Hz, and the high-frequency
roll-off showed the conspicuous behaviour of a single-stage RC-filter (Fig. 10B).
The corner frequency was 8-10 Hz depending on the cell. Already at resting
potential, and even more so above that, the membrane impedance had more
complex behaviour, because of the voltage-dependence. The corner frequency of
the resting membrane was about 20 Hz and that of the depolarized (ca. 10 mV)
membrane around 50 Hz. No correlation was found with the impedance functions
and the variable functional properties of the photoreceptors. The shape and
general behaviour of impedance functions were very similar in all the cells
studied.
67
Voltage (mV)
10
0
Impedance (MΩ)
-10
100
-20
-30
-40
-50
10
Time (ms)
-60
0
100
200
300
400
500
600
0,5
B
0,0
1
10
100
Frequency (Hz)
-0,5
Current (nA)
A
Fig. 10. Membrane properties of photoreceptors, i.e. the membrane filter. A. Typical
current clamp recording showing the responses of the photoreceptor membrane
(upper panel) to current pulses (lower panel). The resting potential of the cell (here 0
mV) was -62 mV. B. Typical impedance functions for the photoreceptor cell membrane
at different voltages. The average recording voltages in relation to the resting
potential (-61 mV) were -25 mV (green), 0 mV (blue), +8 mV (red) and +10 mV (black).
The depolarisations were induced either by constant illumination (+8 mV, red) or by
current injection (+10 mV, black).
5.7
The “axon filter” and spike coding
Although the voltage responses of cockroach photoreceptors to light, especially
impulse responses, are superficially very similar to those found in other insect
species studied, the photoreceptors were found to be able to generate action
potentials (or spikes) in their axons (I, III). The spikes are occasionally seen in
recordings when the electrode tip is located in the retina, but then only as small
peaks of a few millivolts on top of the rising phase of the impulse response (Fig. 2
on page 50; see also Fig. 1 in III). However, when recordings from photoreceptor
axons were made, it was clear that the signalling mode changes in the axons. Both
graded impulse responses and superimposed action potentials are transmitted.
Occasionally several repetitive spikes were observed on top of one impulse
response (Fig. 1C in III and Fig. 6 in I). The spikes could also be triggered by
depolarizing current injections (Fig. 3 in III). Via intracellular staining these cells
68
with graded responses and superimposed spikes were shown to be photoreceptors
(Fig. 2; see also Fig. 2B in III). The spike responses are fairly small, maximally
about 10-20 mV (measured from the top of graded response). The function of the
spiking signals was explained with simplified cable modelling (van Hateren 1986,
van Hateren & Laughlin 1990), with which it could be demonstrated that after
arriving at the distant (up to 1-1,5 mm) presynaptic terminals of photoreceptor
axons in the lamina or medulla, the graded potential is for the most part
attenuated.
5.8
A hypothesis concerning the function of the cockroach visual
system
Based on all the functional variability found in the photoreceptors, and their low
SNR and small channel capacity even when light adapted (I), and the proposed
spike coding in the axons (I, III), a hypothetical model was formulated (I) to
simulate the possible function of the cockroach visual system. Together with the
functional variability, the other foundation of the hypothesis was provided by
previous anatomical findings (Ribi 1977, Ernst & Füller 1987), which show a
significant, but variable amount of pooling of photoreceptor axons (6-20) before
synapse formation in lamina. In addition there are large presynaptic arborisations
in the 2nd order neurons, whose number, on the other hand, is small in comparison
to the large number of photoreceptors.
According to our hypothesis, a group of photoreceptors (with their functional
variability and action potential coding in the axons), when pooled onto the same
2nd order cell, could perform better than a system of photoreceptors with identical
properties. The hypothesis was tested with a simplified computer program, where
the simulations of 12 pooled photoreceptors seemed to support the hypothesis. In
the comparison, even if the identical cells were all of the “best” type (with highest
SNR and coherence) recorded in the experiments, the group of variable
photoreceptors performed better, i.e. produced a more reliable input to the 2nd
order cell.
69
6
Discussion
In this discussion only the main findings of the original articles (I, II, III) are
discussed with respect to vision in dim light. A broader (and in several respects
more detailed) discussion can be found in the original articles themselves.
6.1
Functional variability of the photoreceptors
The large functional variability found in this study (I, II) in many respects
corresponds to structural variability found earlier (Butler 1971, Butler 1973a,
Butler 1973b, Ribi 1977, Ernst & Füller 1987, Füller et al. 1989, Mote 1990,
Ferrell & Reitcheck 1993). Variability is in general highly unusual in visual
systems, where a high level of regularity is thought to be of great importance in
order to form a reliable picture of the surroundings (e.g. French et al. 1977,
Stavenga 1979, Laughlin 1981, Land 1985). According to our results (I, II), the
variability in the function of cockroach photoreceptors seems to be achieved via
structural variations, variations in phototransduction, and variations in light
adaptation. In contrast, as discussed in paper I, and reported in the findings of
paper II, the membrane filtering properties of photoreceptors are always nearly
identical. Hence, variability can’t be explained through differences in the nontranductive membrane.
The structural variation in lens and rhabdom sizes etc. (Butler 1971, Butler
1973a, Butler 1973b, Ferrell & Reitcheck 1993) probably explains most of the
variability in the angular sensitivity (cf. e.g. Snyder & Horridge 1972, Snyder et
al. 1973) and maybe also, at least partly, the variations in the overall sensitivity
(cf. e.g. Snyder et al. 1973, Howard et al. 1987, Roebroek & Stavenga 1990a) of
the photoreceptors. The variations reported between ommatidia in the formation
of the palisade and simultaneous migration of pigment granules (Ferrell &
Reitcheck 1993) can (and probably will) cause variations in photomechanical
adaptation. These can partly explain the variations in the speed of long term
adaptation.
On the other hand, the variations in phototransduction and its adaptational
properties are difficult to explain and even separate from each other. Part of the
variation is probably embedded directly in the processes of photochemical
adaptation, which takes place either inside, and/or in interaction with, the
processes of phototransduction. One possible explanation for the existence of
hyper-adaptation can be given. It has been reported (Krause et al. 2008a) that the
70
light-gated channels in the cockroach photoreceptors are most probably only of
the TRPL-type (transient receptor potential like). In contrast, in Drosophila they
are mostly of the TRP-type (transient receptor potential) with a smaller portion of
the TRPL-type (reviewed by Hardie & Raghu 2001, Hardie 2006, Minke &
Parnas 2006). It is known that in light adaptation TRPL-channels are translocated
out of the rhabdomeric microvilli (Bähner et al. 2002, Meyer et al. 2006). If this
also happens in the cockroach, it would of course lead to a hyper-adaptation,
where light responses disappear. TRP-mutants (lacking TRP-channels) of
Drosophila were originally named because they behaved in light like the “hyperadapting” cells of cockroaches, i.e. they had only transient receptor potentials
(reviewed by Hardie 2006). Their receptor potentials are naturally caused only by
currents through TRPL-channels.
The possible translocation of TRPL-channels from cockroach photoreceptors
might largely explain all the variations in the speed of adaptation. In article II it
was reported that all the cockroach photoreceptors adapt or repolarise close to the
resting potential, when exposed to prolonged (several minutes) illumination. Even
the cells that have the highest values of SNR (about 30% of all photoreceptors)
have a “steady-state” depolarisation of only 10-15 mV after a few minutes of
constant bright light (equivalent to hundreds of thousands of effective photons/s).
The rest of the cells repolarise in the same situation even to a lower value of
depolarisation, most of them within a few millivolts of the resting potential. Even
the former case is not really steady-state, because after tens of minutes of constant
illumination all cells have repolarised close to rest. Time to resting potential in
bright constant light varies from about 400 ms to about 40 min between different
cells. So, in this respect all photoreceptors of the cockroach are “hyper-adapting”,
only with very variable time scales. If the translocation times of TRPL-channels
can also vary this much, this might explain the variations in speed of adaptation.
This might also explain why cockroaches prefer dim environments and avoid long
lasting bright illumination (cf. section 2.3.1) – they become blinded. There is one
finding that speaks against this above depicted TRPL-hypothesis, and that is the
presence of quantum bump-like phenomena during the repolarization phase of the
rapidly adapting photoreceptor responses (see Fig 3A in I and Fig. 1B in II). If the
translocation were responsible for the “hyper-adaptation”, one would expect the
phototransduction gain to decrease uniformly. The presence of quantum bumps
suggests that most of the microvilli are shut off from the transduction process, and
only those remaining are still generating bumps. But surely, this shutting-off
process could be based on the translocation.
71
Variation in spectral sensitivity
One way to further increase the variability of the photoreceptors - and thus
according to our hypothesis on population coding (I), the reliability of the second
order neurons - would be to have variation in the spectral characteristics of the
cells. Mote and Goldsmith (1970, 1971) made their spectral sensitivity recordings
with white-eyed mutant cockroaches and did not report any variability in the
sensitivity curves. Instead, Heimonen (1990) recorded spectral sensitivities of
wild type cockroaches and found considerable variability between photoreceptors
in their spectral properties (Fig. 11).
Fig. 11. Variability in the spectral sensitivity curves of wild type Periplaneta. A.
Spectral sensitivity curves of eight green receptors show variability in the wavelength
of the peak sensitivity and also some variation in their general shape. B. Spectral
sensitivity of UV receptors. C. Photoreceptors having somewhat peculiar spectral
sensitivity curves with three peaks and maximum sensitivities in the UV range were
also encountered more than once. D. The average spectral sensitivity curve based on
all the curves presented in A, B and C shows flatness of sensitivity over the whole
visible spectrum. (Modified from Heimonen 1990.)
The peaks of spectral sensitivity varied both in green (Fig. 11A) and UV receptors
(Fig. 11B). The overall shape of the spectral sensitivity curves also varied and a
couple of times a cell with a three-peaked sensitivity curve was recorded (Fig.
72
11C). It is not being suggested here that these spectrally different photoreceptors
in the cockroach have different photo- or screening pigments, although this is not
completely unknown. For instance this is reported in Mysis shrimps adapted to
different visual environments (Jokela-Määttä et al. 2005, Pahlberg et al. 2005).
However, this would require the necessary genetic machinery to produce these
pigment variations and this is unlikely to exist in the cockroach. Instead, it is
suggested that the variable structure of individual photoreceptors inside
cockroach ommatidia (Butler 1971) is the primary source of this spectral variation.
Photoreceptors with different sizes, shapes and locations of light sensitive
rhabdomeres unavoidably act as spectral screening filters to each other (Snyder et
al. 1973). Even if the rhabdomeres in a fused rhabdom are similar in shape, they
act as lateral screening filters to each other. In cockroaches this filtering has to be
enhanced, since some of the cells having distal rhabdomeres (Butler 1971) modify
the spectral properties of the incident light before more proximal rhabdomeres
have a chance to absorb it. This leads to different spectral sensitivity curves in
different photoreceptors, even though they have exactly the same photopigments.
Of course UV receptors and green receptors (Mote & Goldsmith 1970, Mote &
Goldsmith 1971) have different rhodopsins, but they also unavoidably modify
each other’s spectral sensitivities due to this lateral spectral filtering (Snyder et al.
1973).
What would be the possible benefits of this spectral variation for cockroach
vision? To start with there is considerable confusion in the literature. Butler (1971)
found three UV cells and five green receptors in each ommatidium. Mote (1990)
claimed that all the photoreceptors synapsing directly into the medulla are UV
cells. However, the anatomical studies (Ribi 1977, Ernst & Füller 1987) show that
there is only one photoreceptor per ommatidium terminating in the medulla. This
would leave two UV cells per ommatidium to terminate in the lamina. The
structure of the lamina is so irregular and the bundling of photoreceptor axons so
random that it is difficult to imagine UV and green receptors synapsing separately
and orderly onto their own 2nd order neurons. This leads one to think that they are
pooled onto the same monopolar cells in the lamina. The gain for vision would be
increased variability and thus improved reliability at the level of the 2nd order
cells, and also an enhanced photon catch over a wider spectral range, since the
overall spectral sensitivity of randomly pooled photoreceptors would be nearly
flat (Fig. 11D). This arrangement would still leave room for the reported (Mote &
Rubin 1981, Kelly & Mote 1990b, Mote 1990) UV sensitive cells in the optic
ganglia: some of the UV receptors could still terminate directly in the medulla.
73
6.2
Is functional variability of photoreceptors optimization to low
light levels?
Both previous and present findings show that there are large and uncommon
structural irregularities in the compound eyes of Periplaneta (see section 2.3.2)
and similarly large variability in the functional properties of its photoreceptors (I,
II). Following discussions with colleagues at meetings a possibility has arisen that
these variations could have evolved as degenerative mutations during a long life
in captivity and laboratory breeding (cockroaches are very popular experimental
animals: Huber et al. 1990a, Huber et al. 1990b). This is probably not so, since
during of our experiments we have used hundreds of cockroaches, if not
thousands, from several different colonies from commercial breeders and science
laboratories in different places and the results always include this high variability.
In addition, it has lately been reported that another large dark active cockroach
species (Gromphadorhina portentosa) has similar irregularities in the structure of
its compound eyes (Mishra & Meyer-Rochow 2008), but this species has been
living in laboratory conditions only for a short while. As a conclusion, these
variations are most probably produced by natural evolution and they are likely to
be an adaptation to something.
The behaviour of Periplaneta (see section 2.3.1) strongly supports the
possibility that cockroaches have evolved a dark-active and nocturnal lifestyle
during their long evolution (Kambhampati 1995), and that it has proven to be a
very succesfull strategy (e.g. Bell & Adiyodi 1982, Bell 1990, Bell et al. 2007).
The cockroach visual system may be a secondary sensory information source,
when compared to mechano- and chemosensory systems (e.g. Seelinger 1990, Zill
1990, Mizunami et al. 1998, Ye et al. 2003, Willis & Avondet 2005, Okada & Toh
2006), but nevertheless it is still present in the form of large compound eyes and
ocelli (e.g. Butler 1973a, Mote 1990, Mizunami 1994).
Since sensory systems, especially visual systems, are known to have evolved
to be comparatively well optimized to the ecological niche (e.g. Laughlin 1989,
Laughlin 1990, Weckström & Laughlin 1995, Laughlin 1996, Warrant 1999,
Warrant 2004), it can be concluded that the vision of the cockroach is probably as
equally well adapted. The irregularity and variability are so dominant features of
cockroach vision that they too must be ecologically adapted. Our simulations (I),
which basically combine all the known variability and functional features of the
cockroach visual system, support the idea of optimization to dim light conditions.
They do this by showing that spatial pooling of several of these randomly variable
74
photoreceptors into one 2nd order cell actually enables the visual system to see
more reliably than it could see by pooling an equal number of identical cells. This
is true for all types of photoreceptors encountered in our experiments. In fact our
simulations also showed (unpublished) that the addition of further variability still
further increases the reliability. This supports the possibility that variations in the
spectral sensitivity (see previous section) could also be utilised for this purpose.
However, simulations and modelling, even though useful, can only prove
what is possible. It is surely self-evident that before it can be said that the
cockroach visual system really works as suggested by the simulations, the actual
intracellular recordings from presynaptic photoreceptor axons and 2nd order
neurons are necessary. With the current methods this has up to now been
unsuccessful and an improved preparation technique to enable insertion of a
recording electrode directly into the lamina is under development.
6.3
Is filtering of visual information in cockroach photoreceptors
matched to low levels of luminance?
Only about 30% of the photoreceptors studied here were suitable for reliable
frequency response determinations, since all the others had coherence values
below 0.5 at all the background intensities used (even with time-domain
averaging). These low values of coherence correlated with the low values of
“steady-state” depolarisations. In these cells the frequency response estimates
were always so unreliable, that they are not shown in the figures and were omitted
from all the descriptions of frequency response features.
Nevertheless, all the features of cockroach photoreceptors determined showed
similar temporal features, and almost independent of the light intensity used (II).
The corner frequencies in the transfer functions (above 300 ph/s between 15-25
Hz), constant-light-induced noise power spectra (above 100 ph/s ca. 20 Hz) and
impedance functions (ca. 20 Hz in resting membrane) were all almost exactly
matched to each other. The SNR and coherence functions also had their highest
values only below about these same frequencies. This means the speed of
phototransduction and the properties of the cell membrane seem to be matched.
However, the corner frequency of the membrane impedance rises (up to 5060 Hz) when the membrane is depolarised (Fig. 10B), probably due to increased
activation of voltage-dependent K+ channels. This means that in those conditions
the membrane would allow faster voltage changes than what was recorded in our
experiments. It doesn’t make any sense to enable this without a good reason,
75
since opening more channels to lower the impedance and to raise the corner
frequency costs energy in the form of increased ionic currents (Laughlin et al.
1998, Laughlin 2001, Niven et al. 2007, Niven & Laughlin 2008). These ions then
have to pumped back to maintain the homeostatic concentrations, and this is an
energy consuming process. This allows us to speculate that in transient responses,
during the first second, or even firsts tens of seconds, after a change in light
intensity, the dynamics of the light responses might be better than determined
here. This would occur when the level of depolarisation in the light responses, as
it was in many cells (see Fig. 5), is markedly higher (> 15 mV) than it was during
all our linear dynamics experiments. This would enable higher information
capacities during these transient phases of the responses. The dynamics of the
transient responses can’t be captured with white noise analysis and new kinds of
methods need to be developed to study this.
To summarize, all the filters present in cockroach photoreceptors are more or
less matched to each other’s speeds and they all have fairly low corner
frequencies, even in bright light, especially when compared to diurnal species
(Juusola et al. 1994, Juusola & Hardie 2001a). However, the photoreceptors of
locusts (Faivre & Juusola 2008) and the nocturnal bee Megalopta (Frederiksen et
al. 2008) are about as slow as in the cockroach. Nevertheless, it can be said that
temporally, cockroach vision is matched to low light conditions. In addition, in
most respects (quantum bump size and shape, contrast gain, corner frequencies
etc.) cockroach photoreceptors already reach their peak performance at very low
light intensities (hundreds or thousands of photons/s). When these findings are
combined, it can be said, that the cockroach visual system appears to have been
optimized to cope with low light conditions. Moreover, the inability to improve
performance in any significant manner in bright light can be seen as a permanent
optimization of vision in dim light.
6.4
Is the visual information spike coded in the cockroach
photoreceptor axons?
Usually photoreceptor axons, also in compound eyes, are relatively short (e.g.
Järvilehto & Zettler 1973, Ribi 1979, Ribi 1981, Greiner et al. 2004b) and light
information is generally thought to propagate along them in the form of graded
responses (see e.g. Zettler & Järvilehto 1973, Järvilehto 1978, Weckström 1987).
The axons of cockroach photoreceptors are known to be several times longer than
what is normal in compound eyes (see section 2.3.2). The lamina is usually
76
situated almost immediately below the retina and the basement membrane, but in
the cockroach it is far away from them (see Fig. 2).
In this thesis it was found that the axons of cockroach photoreceptors seem to
utilise spike coding to propagate the signals to the distant lamina (I, III). Action
potentials have also been recorded from other photoreceptors of insect compound
eye, namely in the eyes of the honeybee drone (Baumann 1968). However, in
drones it has been shown that fully fledged action potentials are generated only as
responses to sub-saturating or saturating light stimuli. In physiological conditions
the voltage-gated sodium channels responsible for the spikes in drone
photoreceptors are used to amplify the small membrane voltage modulations that
are generated by the queen bee flying against a bright sky background (Coles &
Schneider-Picard 1989, Vallet & Coles 1991, Vallet et al. 1992, Vallet & Coles
1993). The spikes in the axons of cockroach photoreceptors are different. They
are generated as a response to graded receptor potentials as low in amplitude as 510 mV (I), which means that when dark adapted they might be generated even by
individual quantum bumps. They are small in size (the largest recorded being only
10-20 mV) when measured from the top of the graded response they were
superimposed upon. This can be interpreted in two ways: either the action
potential generation site is (electrically) far away from the site of the recording, or
the intracellular recording electrode causes significant damage to the membrane
and induces a large leak. And of course there is also the third option that the
spikes really are that small. The question as to which of these possibilities is the
right one, can only be solved beyond dispute by further recordings from the axons.
However, with the aid of compartmental Hodgkin-Huxley type model of
cockroach photoreceptors (Salmela et al. 2004) one can show that the generation
site of fully fledged action potentials could be proximal to the second basement
membrane. This would, according to this modelling, be (electrically) far enough
to attenuate the spikes roughly to the same extent as in our experimental data in
papers I and III. In addition, this same study, and a previous study (Heimonen et
al. 2001) with a further improved version of the model in paper III, re-estimated
the attenuation of graded responses while they propagate from the soma to the
distant lamina. It was evaluated that the attenuation is even greater than what was
estimated in article III. Only 10-20% of the amplitude of graded responses was
present, and the higher response frequencies even further attenuated, when the
graded responses reached the synaptic terminal. This further emphasizes the need
for spike coding in the axons.
77
The temporal properties of spike coding were not systematically studied in
any of the original articles in this thesis, since the axon recordings were generally
not stable and sufficiently long lasting for this purpose. It was earlier reported by
Heimonen (1999) that from 15 successful recording sessions from photoreceptor
axons it was possible only twice to quickly record responses to repeated light
flashes with different frequencies (Fig. 12).
Fig. 12. Impulse responses from a cockroach photoreceptor axon to flashes of light
repeated with different frequencies. The frequencies used in each trace were from top
to bottom 5, 8 and 10 Hz. (Heimonen 1999).
In both cells the action potentials were not generated at all when the
frequency of the flashes was raised above 10 Hz, and they were found in less than
a 1:10 relation when stimulating at 10 Hz. Instead, an action potential was
generated by almost every flash at a frequency of 8 Hz, and always below that
frequency. This gives only a rough estimate of the temporal properties of spike
coding. On the other hand, in those recordings where more than one spike per
impulse response were found (Fig. 2, see also Fig. 6 in I), the maximal frequency
of spikes on top of one impulse response was estimated to be about 40-50 Hz.
Noting more is known concerning the temporal properties of spike coding in the
photoreceptor axons of the cockroach.
78
6.5
Are cockroach photoreceptors optimized for vision in dim light?
Even though cockroach compound eyes have large numbers of photoreceptors
(Walther 1958b, Wolken 1971, Nowel 1981, Stark & Mote 1981, Füller et al.
1989), it is argued here that even in bright light the spatial resolution of cockroach
vision is always fairly poor (I). When light-adapted, the spatial resolution of
individual photoreceptors has been shown to sharpen (Butler & Horridge 1973a)
due to palisade formation and intracellular pigment migration, i.e. pupil dilation
(Snyder & Horridge 1972). But this is probably only a side product of overall
sensitivity regulation (e.g. Snyder et al. 1973, Howard et al. 1987, Roebroek &
Stavenga 1990a), necessary to maintain the functionality of photoreceptors even
in bright environments via decreasing the photon catch. However, primarily
because of the likelihood of fairly extensive spatial summation at the first visual
synapse of cockroach compound eyes (Ribi 1977, Ernst & Füller 1987), the
spatial resolution is inevitably low in all illumination conditions. This kind of
strategy of vision can be seen as a permanent optimization favouring dark or dimlight conditions (I).
It is also argued here that the temporal resolution of cockroach vision is
always, even in bright light, fairly poor (II). The temporal resolution of all
compound eyes is fairly low in the dark-adapted state (e.g. Howard et al. 1984,
Laughlin & Weckström 1993, Juusola et al. 1994, Juusola & Hardie 2001a,
Frederiksen et al. 2008), because of the natural need for temporal summation to
make sense of inevitably noisy signals in dim light (e.g. van Hateren 1992,
Warrant 1999, Warrant 2006). It is shown here that cockroach photoreceptors,
unlike other insect visual systems that have been studied (e.g. Howard et al. 1984,
Laughlin & Weckström 1993, Juusola et al. 1994, Juusola & Hardie 2001a,
Frederiksen et al. 2008), fail to speed up with increasing light adaptation (II).
Even in bright light, both temporal resolution and contrast coding abilities stay at
about the same level as they are at intensities of about 1000 photons/s. On the
other hand, if there is no need for faster visual performance in any conditions, this
can be seen as a way to save energy (Laughlin et al. 1998, Laughlin 2001, Niven
et al. 2007, Niven & Laughlin 2008). Faster vision would need faster
phototransduction, i.e. more and smaller quantum bumps, and thus larger amounts
of activated microvilli in the rhabdomeres. It would also mean larger numbers of
opened voltage-gated channels to make the cell membrane faster. Both of these
would lead to an increase in gross ion flux through the membrane, and pumping
ions back to maintain homeostasis would cost energy. Hence, the permanently
79
slow vision of the cockroach (II) can be seen as a permanent optimization
favouring dark or dim-light conditions.
80
7
Conclusions
The processing of visual information in the cockroach Periplaneta americana is
shown to be deviant in many respects from known strategies of neural
information processing in other superficially similar visual systems. All the
uncommon functional features of cockroach photoreceptors found in this thesis
can be seen as adaptations or optimisations to vision in dim light.
1.
2.
3.
The large variability in the functional properties of cockroach photoreceptors,
when combined with a fair amount of neural pooling in the first visual
synapse, enables the cockroach visual system to function more reliably than
with functionally identical cells.
The temporal dynamics and light information filtering properties of
cockroach photoreceptors seem to be permanently matched or optimised to
low levels of illumination. The information processing capabilities of all
types of cockroach photoreceptors were, at all levels of light adaptation (even
in bright light), very similar to processing capabilities in dim light, i.e.
equipped with slow dynamics and low SNR.
Spike coding is introduced in cockroach visual processing in an astonishingly
early phase, i.e. already in the photoreceptor axons. When combined with
functional variability and neural pooling in the first visual synapse this
feature on its own further increases the reliability of the cockroach visual
system in dim environments.
The cockroach visual system, at least when judged on the basis of the function of
the photoreceptors, seems to be permanently optimised to operate in dim light: the
functional capacity is no better in bright light than at very low levels of ambient
illumination.
81
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