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First published online May 19, 2008
Journal of Experimental Biology 211, 1819-1828 (2008)
Published by The Company of Biologists 2008
doi: 10.1242/jeb.016402
The adaptive evolution and processing of sensory systems |
Computational mechanisms of mechanosensory processing in the cricket
Center for Computational Biology, 1 Lewis Hall, Montana State University, Bozeman, MT 59717, USA
* Author for correspondence (e-mail: gwen{at}cns.montana.edu)
Accepted 28 March 2008
Summary
Crickets and many other orthopteran insects face the challenge of gathering sensory information from the environment from a set of multi-modal sensory organs and transforming these stimuli into patterns of neural activity that can encode behaviorally relevant stimuli. The cercal mechanosensory system transduces low frequency air movements near the animal's body and is involved in many behaviors including escape from predators, orientation with respect to gravity, flight steering, aggression and mating behaviors. Three populations of neurons are sensitive to both the direction and dynamics of air currents: an array of mechanoreceptor-coupled sensory neurons, identified local interneurons and identified projection interneurons. The sensory neurons form a functional map of air current direction within the central nervous system that represents the direction of air currents as three-dimensional spatio-temporal activity patterns. These dynamic activity patterns provide excitatory input to interneurons whose sensitivity and spiking output depend on the location of the neuronal arbors within the sensory map and the biophysical and electronic properties of the cell structure. Sets of bilaterally symmetric interneurons can encode the direction of an air current stimulus by their ensemble activity patterns, functioning much like a Cartesian coordinate system. These interneurons are capable of responding to specific dynamic stimuli with precise temporal patterns of action potentials that may encode these stimuli using temporal encoding schemes. Thus, a relatively simple mechanosensory system employs a variety of complex computational mechanisms to provide the animal with relevant information about its environment.
Key words: cricket, mechanoreception, neural coding, neural maps
Introduction
Animals within their sensory environments face the challenge of transducing
and interpreting relevant sensory information in order to enact the
appropriate behavioral responses to the stimuli. Neuroscientists who wish to
understand how sensory systems accomplish these tasks are faced with three
major challenges: (1) to understand the relationships between spatio-temporal
activity patterns in sensory neural ensembles and the information they convey,
(2) to understand how the spatio-temporal patterns are decoded by cells at the
next processing stage, and (3) to understand how computations (e.g. pattern
recognition) are carried out on that decoded information. These challenges are
difficult or impossible to separate from one another in many cases, since the
functions of representation, decoding and computation are often concatenated.
For example, in the human visual system, the transformation in the spatial
representation of visual space between the retina and the primary visual
cortex is thought to enable a computation carried out by postsynaptic cells:
the complex log transformation of visual images
(Schwartz, 1994
). Over the
last several decades, researchers in several institutions around the world
have been studying these problems with considerable success in a much simpler
mapped sensory system: the cercal sensory system of the cricket. The cercal
system is implemented around a representation of stimulus direction and
dynamics, and demonstrates the essential features of neural maps found in more
complex systems, including mammalian visual and auditory systems. This review
will discuss progress toward understanding the structure and operation of the
cercal system within the context of the neural computations it mediates, and
summarize insights into the mechanisms through which information-processing
algorithms are implemented within this system. These insights may well be
generalized to more complex systems.
Overview of the cercal sensory system
Every orthopteran insect has a cercal sensory system, which mediates the
detection, localization and identification of air currents. The receptor
organs for this modality are two antenna-like appendages called cerci at the
rear of the abdomen, covered with mechanosensory hairs. Air currents in the
animal's immediate environment move these hairs and, thereby, activate the
receptor neurons at the base of the hairs.
Fig. 1 shows the basic
structure of the cercal sensory system in Acheta domestica: the
cerci, mechanosensory afferent neurons and the projection interneurons.
|
; Camhi,
1980) and jumping (Hoyle,
1958). However, it is a considerable oversimplification to
classify this system as an `escape system', just as it would be an
oversimplification to categorize our own visual system with that label.
Rather, the cercal system can be thought of as functioning as a low-frequency,
near-field extension of the animal's auditory system. The emerging picture of
the cercal system and its associated behaviors is fairly complex. Air particle
displacement directed at the cerci can elicit at least 14 distinct responses,
including evasion, flight, offensive reactions, scanning, freezing, and
various reactions during male stridulation, and the response can depend on the
behavioral state of the animal as well as the context of the environment
(Baba and Shimozawa, 1997). In
addition to sensitivity to air particle displacement stimuli mediated by the
filiform receptors we study, the cercal system is also responsible for
mediating behavior related to orientation relative to gravity as well as a
variety of touch and chemotactic sensations via other types of sensor
on the cerci (Sakaguchi and Murphey,
1983; Murphey,
1985; Heusslein and Gnatzy,
1987). Our discussion will be limited to the filiform receptors
sensitive to air particle displacement.
Filiform mechanoreceptor array
In the common house cricket Acheta domestica, which is the species
we study, each cercus is approximately 1 cm long in a normal adult cricket,
and is covered with approximately 750 filiform mechanosensory hairs. These
hairs range in length from less than 50 µm to almost 1.5 mm
(Landolfa and Miller, 1995).
Each hair is supported at its base by a viscoelastic socket that enables the
hair to pivot within its socket, rather than bending along its shaft
(Thurm, 1964;
Thurm, 1965a;
Thurm, 1965b;
Thurm, 1983;
Thurm and Kuppers, 1980). Each
hair's directional movement axis is determined by the orientation of a
hinge-like structure in the socket. The 750 hairs on each cercus are arrayed
with their movement axes in diverse orientations within the horizontal plane,
insuring that air currents of sufficient velocity will deflect all of the
filiform hairs to some extent: each hair will be deflected from its rest
position by an amount that is proportional to the cosine of the angle between
the air current direction and the hair's movement axis.
Unlike the mammalian cochlea, where efferent neural feedback can fine-tune
the responsiveness of the auditory transducers, mechanical filtering of air
current stimulus amplitude and frequency in the cercal system is determined
solely by the biomechanical configuration of the hairs
(Kanou and Shimozawa, 1984;
Kumagai et al., 1998;
Osborne, 1997;
Shimozawa and Kanou, 1984a;
Shimozawa and Kanou, 1984b;
Shimozawa et al., 1998;
Cummins et al., 2007;
Cummins and Gedeon, 2007;
Gedeon et al., 2007;
Heys et al., 2008).
Specifically, the primary determinants of each hair's frequency filtering
properties are its length, mass and the viscoelastic properties of its socket:
these properties determine the hair's moment of inertia, spring stiffness, and
extension into the boundary layer of moving air surrounding the cercus.
The movements of the ensemble of hairs will therefore depend on the direction and dynamics of the air current. Thus, the internal representation of any particular air current (e.g. the air current caused by the wing beats of an approaching predatory wasp) will be a complicated spatio-temporal pattern of activity across the entire array of synaptic arborizations of these filiform afferents within the cricket's nervous system.
Since the beginning of studies of the cercal sensory system, researchers
have noted the extremely low inter-animal variability in the placement and
characteristics of the filiform hairs
(Landolfa and Jacobs, 1995;
Landolfa and Miller, 1995;
Walthall and Murphey, 1986).
The importance of the cerci for the animal's survival, the coupling between
cercal structure and function, and the extremely low inter-animal variability
of cercal receptor hair array structure are all consistent with the conjecture
that these structural attributes have been subject to substantial selective
pressure, and may be nearly optimal from an engineering standpoint, if only we
could determine the appropriate, behaviorally relevant metrics for
optimality.
Sensory receptor neurons
Each mechanosensory hair is innervated by a single spike-generating
mechanosensory receptor neuron. These receptors display directional and
dynamical sensitivities that appear to be derived largely from the mechanical
properties of the hairs themselves
(Humphrey et al., 1993;
Kämper and Kleindienst,
1990; Landolfa and Jacobs,
1995; Landolfa and Miller,
1995; Roddey and Jacobs,
1996; Shimozawa and Kanou,
1984a; Shimozawa and Kanou,
1984b). The amplitude of the response of each sensory receptor
cell to any air current stimulus depends upon the direction of that stimulus,
and these directional tuning curves of the receptor afferents are well
described by cosine functions (Landolfa
and Jacobs, 1995; Landolfa and
Miller, 1995). The response amplitudes also depend upon the
frequency composition of the stimulus waveforms, and generally follow the
trend that would be predicted from the mechanical filtering properties of the
hairs: receptors innervating long mechanoreceptor hairs (>900 µm) are
most sensitive to low frequency air currents (<150 Hz), and receptors
innervating medium length hairs (500–900 µm) are most sensitive to
frequency ranges between 150 and 400 Hz
(Roddey and Jacobs, 1996;
Shimozawa and Kanou, 1984a;
Shimozawa and Kanou, 1984b).
Receptors innervating the shortest hairs (50–500 µm) respond to
frequencies up to 1000 Hz.
Internal representation of air current direction and dynamics
The axons of the receptor afferents project in an orderly array into the
terminal abdominal ganglion to form a continuous representation (i.e. neural
map) of the direction of air currents in the horizontal plane
(Bacon and Murphey, 1984;
Jacobs and Theunissen, 1996;
Jacobs and Theunissen, 2000;
Paydar et al., 1999). That is,
the afferent synaptic terminals form an ordered array across which there is a
continuous, systematic variation in the value of their peak sensitivities to
air current direction. The synaptic terminals from afferents having similar
peak directional sensitivities arborize in adjacent areas, and the spatial
segregation between afferent arbors increases as the difference in their
directional tuning increases.
The systematic mapping of stimulus direction across a subset of the
afferents has been demonstrated in several recent studies in which anatomical
and physiological measurements were taken from a representative sample of
afferents near the base of the cerci proximal to the terminal abdominal
ganglion (Jacobs and Theunissen,
1996; Paydar et al.,
1999; Troyer et al.,
1994). Anatomical reconstruction of the afferent arborizations was
used to construct a three-dimensional model of this proximal portion of the
afferent map in the form of a probabilistic atlas. This basic structure is
shown in Fig. 2. By combining
the predicted responses of each class of afferent with this information about
the spatial location of their terminal arborizations within the neural map,
predictions have been made of the spatial patterns of synaptic activation that
would result from sustained, unidirectional air currents
(Jacobs and Theunissen, 2000;
Paydar et al., 1999;
Troyer et al., 1994).
Recently, Ogawa and colleagues actually visualized ensemble activity patterns
of filiform afferents using calcium imaging
(Ogawa et al., 2006), and
found these patterns to be consistent with the predictions.
|
; Cummins et
al., 2003). However, these predictions of the dynamic response
patterns may significantly underestimate the complexity of the actual dynamic
response patterns, since they did not take differential conduction delay along
the cercus into account. As we have recently demonstrated, spikes arriving at
the terminal abdominal ganglion from distal cercal mechanoreceptors have a
significantly greater latency than spikes arriving from more proximal
receptors, due to the added conduction time along the length of the cercus
(Kennel et al., 2005). This
`dispersion' of the spiking input from afferents, which would otherwise have
identical directional and dynamic response characteristics, could be of
considerable functional significance. In other words, the cercus is acting as
a `delay line'. We are currently investigating the differential anatomical
projections of these distal receptors, and examining the functional
significance of these delay-line characteristics. Preliminary evidence
suggests that the primary sensory interneurons could, in fact, extract and use
this delay information.
Primary sensory interneurons
The 1500 sensory afferents synapse with a group of approximately 30 local
interneurons, and approximately 20 identified projecting interneurons that
send their axons to motor centers in the thorax and integrative centers in the
brain. It is important to note that these 20 or so projecting interneurons
represent the entire ensemble for all information captured by the 1500 sensory
receptors and transmitted to higher processing stages. This represents a huge
compression.
Like the afferents, these interneurons are also sensitive to the direction
and dynamics of air current stimuli
(Jacobs et al., 1986;
Kanou and Shimozawa, 1984;
Miller et al., 1991;
Theunissen and Miller, 1991;
Theunissen et al., 1996).
Researchers have measured stimulus-evoked neural responses in several
projecting and local interneurons, using several different classes of air
current stimuli and electrophysiological techniques. Recently, optical
recording techniques using Ca2+-sensitive dyes have also been used
to examine the mechanisms underlying synaptic integration in cercal
interneurons (Ogawa et al.,
2006; Ogawa et al.,
2008). The stimuli that have been used range from simple
unidirectional air currents to complex multi-directional, multi-frequency
waveforms. Two important general conclusions from all of these studies are as
follows.
In the remainder of this review, we will focus on our recent analyses of neural coding at this interface between the sensory afferents and the primary sensory interneurons, using some analytical approaches from information theory and statistics.
Neural coding in the cricket cercal system
Tuning curve analyses
Neural coding is defined as the mapping between stimuli in the environment
and their representation in patterns of electrical activity in the nervous
system. The earliest efforts toward discovering the nature of these mappings
stretch back over 80 years to the work of Adrian and colleagues on stretch
receptors in the leg muscles of frogs
(Adrian and Zotterman, 1926).
They employed what is now known as the tuning curve technique: a stimulus is
presented to the nervous system in which a single parameter can be
systematically varied over the course of several trials. For each presentation
of the stimulus with a specific value of the parameter of interest, an output
of the nervous system is measured, generally a count of the number of action
potentials in a small window of time during or after the stimulus
presentation. Another tuning curve metric is to measure the time to the first
response that can be discriminated or, inversely, the mapping is reported as
the minimum value of a stimulus parameter that elicits any activity in the
nervous system. Solving the neural coding problem is then just reduced to
determining the input–output relationship defined by the function
relating the values of the stimulus parameter to the measured output of the
cell. An example of such a relationship is shown in
Fig. 3: the directional tuning
curve of interneuron right 10-2a from Acheta domestica.
|
Although this method is simple to use and yields a good first-order assessment of neural function, it relies on two rather strict sets of assumptions. First, it is assumed that a single, generally static parameter of the stimulus is important for the nervous system. This can be a problem when the nervous system actually depends on a dynamic stimulus, or a stimulus that cannot be easily parameterized. Second, the choice of how the response is measured necessarily requires a strong assumption about the nature of the code itself, usually that the information about the stimulus is completely contained in the firing rate.
The method has been extensively used to demonstrate the directionality of
the cercal system, beginning with the use of directional oscillatory sound
stimuli (Tokareva and Rozhkova,
1973; Edwards and Palka,
1974; Palka et al.,
1977; Palka and Olberg,
1977), and later being refined to stimuli consisting of single
puffs of air (Westin et al.,
1977; Tobias and Murphey,
1979; Westin,
1979; Aldworth et al., 2008;
Miller and Jacobs, 1984;
Jacobs and Miller, 1985;
Jacobs et al., 1986;
Miller et al., 1991;
Theunissen and Miller, 1991;
Bodnar et al., 1991;
Baba et al., 1991;
Baba et al., 1995;
Kolton and Camhi, 1995) (Z.A.,
A. G. Dimitrov, G. Cummins, T. Gedeon and J.P.M., manuscript submitted). In
one set of experiments, it was shown that two bilateral pairs of interneurons
(10-2a and 10-3a) formed a functional unit capable of detecting air particle
displacement at moderate velocities from all 360° of space in the
horizontal plane (Miller et al.,
1991; Theunissen and Miller,
1991). Similarly, by statistically sampling the cercal afferent
population, Landolfa and colleagues were able to determine the directional
sensitivity of the entire afferent array
(Landolfa and Jacobs, 1995).
These population-level tuning curves have in turn led to theoretical work on
how neural coding is implemented in populations of neurons, both in
interneurons (Salinas and Abbott,
1994; Butts and Goldman,
2006) and afferents (Ergun et
al., 2007).
These studies spanned the system from the receptor level to the local and
projecting interneurons of the terminal abdominal ganglia, though most of the
studies with air particle displacement stimuli (rather than oscillatory sound
stimuli) focused on interneurons 10-2a and 10-3a. For the sake of
completeness, average tuning curves elicited by air particle displacement
stimuli for 222 cells (127 of whose morphology was confirmed by staining) from
classes 8-1a, 9-1a, 9-1b, 10-1a, 7-2a, 8-2a, 9-2a, 9-3a, 10-2a and 10-3a are
shown in Fig. 4. Interneuron
11-1a was never recorded from in over 500 recordings, but it has been reported
to be sensitive to multiple stimulus directions and especially stimuli with
angular velocity [i.e. vortices termed 11-1c (see
Kämper, 1984) and NGI-5
(see Baba et al., 1991)].
Fig. 4C shows the peak
directions for all of the giant interneurons with uni-modal tuning.
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), the four interneurons composed of left and right 10-2a and
left and right 10-3a form a functional unit sensitive to low velocity air
displacement from all 360° of directional space in the horizontal plane
(Fig. 4Biii). Based on observed
stimulus–response measurements, information theoretic calculations
demonstrated that the ensemble responses of these four interneurons to
uni-directional air puffs contain enough information that an `upstream'
decoder could determine the direction of the source with an accuracy of about
10°, even if that decoder were limited to a simple ratio of spike counts
as the operative neural coding scheme
(Miller et al., 1991;
Theunissen and Miller, 1991).
It has been speculated that interneurons 9-2a and 9-3a may form a second such
functional unit sensitive to higher velocities of air movement
(Miller et al., 1991). Giant
interneuron GI 7-2a and probably GI 8-2a receive input from the clavate hairs
and so are sensitive to acceleration due to gravity
(Sakaguchi and Murphey, 1983).
But here and in other studies
(Kämper, 1984;
Kohstall-Schnell and Gras,
1994) GI 7-2a was also found to respond to air particle
displacement at sufficiently large stimulus levels. Along with 8-2a, 7-2a has
directional tuning such that the two pairs of cells could form yet another
functional unit, sensitive to very high intensity air particle displacement
(Fig. 4Bi).
White noise, kernels, stimulus reconstruction and information rates
A second approach to the coding problem is the `white noise' approach of
Wiener, popularized for use in neuroscience by Marmarelis, Naka and colleagues
(Wiener, 1958;
Marmarelis and Naka, 1972).
The goal in this methodology is to predict the output of a potentially
non-linear system to a stochastic input signal, usually Gaussian white noise
(GWN). The white noise approach was first used to study the cercal system in
experiments on the afferent layer and local non-spiking interneurons of the
cockroach (Kondoh et al.,
1991a; Kondoh et al.,
1991b). For the afferent layer the first- and second-order kernels
together provided a very good fit to experimental data, while in the local
interneuron the first- and second-order filters only combined to describe
about half of the neural response (the contribution of the second-order filter
was negligible).
|
;
Rieke et al., 1997). The
mathematics is essentially the same, though in this case the neural response
becomes the input variable, while the `output variable' that is being
estimated is the stimulus. This approach has been called `reverse
reconstruction' to denote its inversion of the more traditional white noise
analyses. This type of analysis enables elements of information theory to be
applied to the problem of characterizing a neuron's input–output
characteristics, and allows quantitative calculations of the amount of
information that could be decoded from a neuron's spike trains, in terms of
bits (Shannon, 1968; Cover and Thomas,
1991), given a set of assumptions about that cell's encoding
scheme. This approach has been used to investigate coding in the first two
layers of the cricket cercal system, yielding valuable insights. One study of
frequency sensitivity to broadband (10–400 Hz) stimuli concluded that
all four cells in one functional ensemble (left and right 10-2a and 10-3a)
have identical frequency tuning, concentrated in the 10–50Hz range
(Theunissen et al., 1996). An
associated study applied the same information theoretic approach to analyze
dynamical encoding characteristics of filiform mechanoreceptors. Fig. 5 shows several aspects of this type of analysis of data from interneurons 10-2a and 10-3a. The cell under study was stimulated with a dynamically changing air current, directed along the axis corresponding to that cell's peak sensitivity. The velocity of the air current varied according to a band-limited (10–200 Hz) GWN function. The spike train elicited by this stimulus was recorded (Fig. 5A). The first-order Volterra kernel (also referred to commonly as the first-order `stimulus reconstruction' kernel) was then extracted (Fig. 5B). This kernel corresponds to the best estimate of the average stimulus waveform leading up to a single spike, if the coding is linear. Within the context of neural coding, the notion of linearity does not refer to the spike-generation process itself: it is well known that the Hodgkin–Huxley equations are non-linear, i.e. that doubling the current input to a cell will yield a voltage response that is not necessarily double. Rather, linearity of coding implies that all of the information in patterns of two or more spikes can be decoded by analyzing `one spike at a time', i.e. that the information in spikes is additive. In other words, the `meaning' of a short-interval doublet of spikes would correspond to the `meaning' of two single spikes offset by the observed doublet interval. It is certainly possible to have a non-linear process like spike generation serving as the basis for the production of a linear coding scheme (e.g. rate coding), since it is a change in the spike rate, not the spike shape, that encodes the information.
The kernel waveform is interesting in and of itself: it yields an estimate of the aspect of the stimulus that leads to a spike. These kernels can also be used to quantify the `performance characteristics' of a cell under the assumption of linearity, by (a) obtaining an estimate of the entire stimulus waveform that was presented to the cell, and then (b) comparing that estimate with the actual waveform that had, in fact, been presented. The approach is conceptually very simple: starting with the observed spike train elicited in the experiment, construct an estimated stimulus waveform by `stamping down' an image of the reconstruction kernel every time a spike occurred, and add up all of the kernel waveforms. In regions where spikes are isolated by intervals longer than the duration of the kernel, the estimated stimulus waveform will simply be a kernel-shaped bump in the stimulus waveform. Where there are sequences of spikes that come very close together in time, the kernels will overlap, and the summation over the kernels will yield a complex waveform. The next step of the analysis is simply to compare the estimated stimulus waveform with the actual waveform: if the estimate and actual stimulus are superimposable, then the cell was `perfectly' encoding the stimulus waveform. This is, of course, never the case: the estimate will deviate from the actual stimulus in some respects. The extent to which the estimate deviates from the real stimulus yields a quantitative measure of (a) the limitations of the cell in capturing the information about the stimulus, (b) the `noise' in the encoding process, and (c) the possible errors in assumptions about the encoding scheme. In the studies of the cricket interneurons, it was found that stimulus waveform could be decoded with relatively high fidelity using a linear decoder, but only within a relatively narrow frequency range (10–70 Hz).
|
).
Briefly, the gain function represents the encoding characteristics of the
neuron as a function of frequency: the higher the gain at a particular
stimulus frequency, the more information is encoded by that cell about that
frequency. A gain of 1 would correspond to a perfect match or
superimposability between the estimated and actual stimuli, and thus indicate
a `perfect' encoder, zero noise, and a valid set of assumptions. A gain of
zero would indicate a substantial problem in one or more of these three
aspects of the analysis. Fig.
5D demonstrates the narrow band encoding mediated by this cell:
this neuron encodes information about the direction of air current stimuli,
but only for those stimuli with frequency components within the 10–50 Hz
range.
Non-linear encoding
As mentioned above, the kernel-based analyses of the cercal sensory system
were based on the assumption that neural coding is a linear process in those
cells. This is equivalent to the assumption that the best estimate of the
stimulus waveform leading up to the doublet is what you would predict by
adding up two copies of the kernel for a single isolated spike
(Fig. 5B), offset by the
doublet interval, as described above. Aldworth and colleagues
(Aldworth et al., 2005) (Z.A.,
A. G. Dimitrov, G. Cummins, T. Gedeon and J.P.M., manuscript submitted) have
just demonstrated that this is not the case.
Fig. 6 demonstrates that the
reverse reconstruction method (based on the use of the kernel approach
assuming linearity) grossly underestimates the information content in the
neural spike train, as assayed by a model-independent approach called the
`direct method' (Strong et al.,
1998; Victor,
2002; Paninski,
2003; Nemenman et al.,
2004; Kennel et al.,
2005; Shlens et al.,
2007). This figure also reports the interesting result that the
cricket interneurons are transmitting information about stimulus dynamics at
rates of up to 130 bits s–1. Through a novel analytical
approach, Aldworth determined a range of spike doublets that are in essence a
different neural symbol from two single spikes, and derived the kernel for
these doublets. His new approach also corrected a substantial source of error
implicit in earlier approaches toward kernel extraction, and enables the
neurophysiologist to get an accurate estimate of the temporal resolution of
neural encoding. For the cricket sensory interneurons we study, the temporal
precision is of the order of 5 ms. In other words, a higher-order circuit
would not be able to decode the time of occurrence of an event that elicited a
spike in that neuron with a temporal precision better than 5 ms.
Conclusions
Our general concept of the functional `engineering design' of the cercal
system, derived from all of the work that has been done on this system in our
lab and other labs over the last few decades, supports the concept of the
cercal system being a generalist system (like an auditory or visual system)
rather than a specialized feature-detection and escape system using `command
neurons' (like the lateral giant interneurons in the escape system in crayfish
or the Mauthner cells in zebra fish). That is, there is no strong evidence for
feature detector cells at either the receptor or first-order interneuron
stages of processing. Instead, the filiform mechanoreceptor array captures a
very well-sampled image of the air current field surrounding the animal, and
represents the image of that field as activity across a continuous map of that
field in the terminal abdominal ganglion (TAG), in the same sense that the
information from our own retinae project a continuous map of visual space into
our visual cortex. The first-order sensory interneurons that extract
information from this sensory map at this first processing stage are also
generalists: the computations these interneurons appear to carry out include
(at least) the following operations: (a) noise reduction through signal
averaging across many sensory afferents, (b) extremely efficient re-encoding
of the direction of air current stimuli, via a huge dimensional
reduction of the activities of the 1500 sensory afferents down to a
four-interneuron ortho-normalized code
(Theunissen and Miller, 1991;
Salinas and Abbott, 1994), (c)
coding of the spectral composition of dynamic air currents via the
relative activity levels of different interneurons having different frequency
sensitivity bands, and (d) (still speculative) the representation of the curl
of air currents via interneurons sensitive to vortices rather than
linear air streams. The information available at this first-order sensory
interneuron interface is extraordinarily good from an engineering perspective,
in terms of the temporal and angular accuracy and precision. Based on this
generalist information represented at the first stage of processing, more
complex processing operations (such as feature detection and target
identification) are, presumably, computed at higher levels of the nervous
system by more specialized cells and circuits.
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