Dynamics of the ganglion cell response in the catfish and frog retinas

Seeing via Miniature Eye Movements: A Dynamic Hypothesis for Vision

During natural viewing, the eyes are never still. Even during fixation, miniature movements of the eyes move the retinal image across tens of foveal photoreceptors. Most theories of vision implicitly assume that the visual system ignores these movements and somehow overcomes the resulting smearing. However, evidence has accumulated to indicate that fixational eye movements cannot be ignored by the visual system if fine spatial details are to be resolved. We argue that the only way the visual system can achieve its high resolution given its fixational movements is by seeing via these movements. Seeing via eye movements also eliminates the instability of the image, which would be induced by them otherwise. Here we present a hypothesis for vision, in which coarse details are spatially encoded in gaze-related coordinates, and fine spatial details are temporally encoded in relative retinal coordinates. The temporal encoding presented here achieves its highest resolution by encoding along the elongated axes of simple-cell receptive fields and not across these axes as suggested by spatial models of vision. According to our hypothesis, fine details of shape are encoded by inter-receptor temporal phases, texture by instantaneous intra-burst rates of individual receptors, and motion by inter-burst temporal frequencies. We further describe the ability of the visual system to readout the encoded information and recode it internally. We show how reading out of retinal signals can be facilitated by neuronal phase-locked loops (NPLLs), which lock to the retinal jitter; this locking enables recoding of motion information and temporal framing of shape and texture processing. A possible implementation of this locking-and-recoding process by specific thalamocortical loops is suggested. Overall it is suggested that high-acuity vision is based primarily on temporal mechanisms of the sort presented here and low-acuity vision is based primarily on spatial mechanisms.

Dopamine receptor activation can reduce voltage-gated Na+ current by modulating both entry into and recovery from inactivation

We tested whether dopamine receptor activation modulates the voltage-gated Na+ current of goldfish retinal ganglion cells, using a fast voltage-clamp amplifier, perforated-patch whole cell mode, and a physiological extracellular Na+ concentration. As found in other cells, activators of D1-type dopamine receptors and of protein kinase A reduced the amplitude of current activated by depolarizations from resting potential without altering the current kinetics or activation range. However, D1-type dopamine receptor activation also accelerated the rate of entry into inactivation during subthreshold depolarizations and slowed the rate of recovery from inactivation after single, brief depolarizations. Our results provide the first evidence in any preparation that D1-type receptor activation can produce both of these latter effects.

Spike generator limits efficiency of information transfer in a retinal ganglion cell

The quality of the signal a retinal ganglion cell transmits to the brain is important for preception because it sets the minimum detectable stimulus. The ganglion cell converts graded potentials into a spike train with a selective filter but in the process adds noise. To explore how efficiently information is transferred to spikes, we measured contrast detection threshold and increment threshold from graded potential and spike responses of brisk-transient ganglion cells. Intracellular responses to a spot flashed over the receptive field center of the cell were recorded in an intact mammalian retina maintained in vitro at 37 degrees C. Thresholds were measured in a single-interval forced-choice procedure with an ideal observer. The graded potential gave a detection threshold of 1.5% contrast, whereas spikes gave 3.8%. The graded potential also gave increment thresholds approximately twofold lower and carried approximately 60% more gray levels. Increment threshold "dipped" below the detection threshold at a low contrast (<5%) but increased rapidly at higher contrasts. The magnitude of the "dipper" for both graded potential and spikes could be predicted from a threshold nonlinearity in the responses. Depolarization of the cell by current injection reduced the detection threshold for spikes but also reduced the range of contrasts they can transmit. This suggests that contrast sensitivity and dynamic range are related in an essential trade-off.

A context-free data compression approach to measuring information transmission by action potentials

Action potentials allow nervous systems to transmit information rapidly and efficiently over considerable distances, but what is the information they carry and how much can be carried by one neuron? Often, qualitative and vague descriptions are used, such as the firing rate representing intensity. Recent attempts to quantify information transmission by action potentials have concentrated on treating neurons as communication channels, whose information capacity can be estimated from their signal-to-noise ratios. However, this only indicates how much information could theoretically be carried, not the actual amount at any given time, and the ratio itself depends on assumptions about information coding. Here we introduce a different approach based on the concept of data compression, which has become familiar with the widespread use of digital computers and networks. Compression takes advantage of redundancy in a sequence of numbers to reduce its size, but allows it to be reconstructed later without error. We show that data compression by a context-free grammar can quantitatively estimate the real information content of action potential signals without any prior assumptions about coding, or knowledge of neural inputs. Measurements of information coding by mechanosensory neurons are used as examples, but a major advantage of this approach is its generality. It can estimate information transmission by any neuron whose output can be measured, regardless of neuronal type, connectivity or function.

Figuring space by time

Sensory information is encoded both in space and in time. Spatial encoding is based on the identity of activated receptors, while temporal encoding is based on the timing of activation. In order to generate accurate internal representations of the external world, the brain must decode both types of encoded information, even when processing stationary stimuli. We review here evidence in support of a parallel processing scheme for spatially and temporally encoded information in the tactile system and discuss the advantages and limitations of sensory-derived temporal coding in both the tactile and visual systems. Based on a large body of data, we propose a dynamic theory for vision, which avoids the impediments of previous dynamic theories.

Predicting every spike: a model for the responses of visual neurons

In the early visual system, neuronal responses can be extremely precise. Under a wide range of stimuli, cells in the retina and thalamus fire spikes very reproducibly, often with millisecond precision on subsequent stimulus repeats. Here we develop a mathematical description of the firing process that, given the recent visual input, accurately predicts the timing of individual spikes. The formalism is successful in matching the spike trains from retinal ganglion cells in salamander, rabbit, and cat, as well as from lateral geniculate nucleus neurons in cat. It adapts to many different response types, from very precise to highly variable. The accuracy of the model allows a compact description of how these neurons encode the visual stimulus.

Processing of color- and noncolor-coded signals in the gourami retina. III. Ganglion cells

The dynamics of intracellular responses from ganglion cells, as well as that of spike discharges, were studied with the stimulus regimens and analytic procedures identical to those used to study the dynamics of the responses from horizontal and amacrine cells (,). The stimuli used were large fields of red and green light given as a pulsatile input or modulation about a mean luminance by a white-noise signal. Spike discharges evoked by a white-noise stimulus were analyzed in exactly the same manner as that used for analysis of analog responses. The canonical nature of kernels allowed us to correlate the first- and second-order components in a spike train with those of the intracellular responses from horizontal, amacrine, and ganglion cells. Both red and green stimuli given alone in darkness produced noncolor-coded responses from all ganglion cells. In the case of some cells, steady red illumination changed the polarity or waveform of the response to green light. Color-coded ganglions responded only to simultaneous color contrast. Nonlinearities recovered from intracellular responses, and spike discharges were similar to those found in responses from amacrine cells and were of two types, one characteristic of the C amacrine cells and the other characteristic of the N amacrine cells. The first-order kernels of most ganglion cells could be divided into two basic types, biphasic and triphasic. The combination of kernels of these two basic types with different polarities can produce a wide range of responses. Addition of two types of second-order nonlinearity could render color coding in this relatively simple retina as an extremely complex process. Color information appeared to be represented by the polarity, as well as the waveform, of the first-order kernel. The response dynamics is a means of transmission of color-coded information. Second-order components carry information about changes around a mean luminance regardless of the color of an input. Some spike discharges produced a well-defined cross-kernel between red and green inputs to show that a particular time sequence of red and green stimuli was detected by the retinal neuron network. The similarity between signatures of second-order kernels for both amacrine and ganglion cells indicates that signals undergo a minimal transformation in the temporal domain when they are transmitted from amacrine to ganglion cells and then transformed into a spike train. Under our experimental conditions, a single spike train carried simultaneously information about red and green inputs, as well as about linear and nonlinear components. In addition, the spike train also carries a cross-talk component. A spike train is a carrier of multiple signals. Conversely, many types of information in a stimulus are independently encoded into a spike train.

The efficiency of sensory information coding by mechanoreceptor neurons

Most sensory systems encode external signals into action potentials for transmission to the central nervous system, but little is known about the cost or efficiency of this encoding. We measured the information capacity at three stages of encoding in the neurons of a spider slit-sense mechanoreceptor organ. For the receptor current under voltage clamp, the capacity was approximately 1400 bits/s, but when the neuron was allowed to generate a receptor potential, nonlinear membrane processes improved the capacity to >2000 bits/s. Finally, when action potentials were produced, the capacity dropped to approximately 200 bits/s, or approximately 14% of the receptor current capacity. These measurements provide a quantitative estimation of the cost of encoding analog signals into action potentials.

Dynamics of neurons controlling movements of a locust hind leg. III. Extensor tibiae motor neurons

Imposed movements of the apodeme of the femoral chordotonal organ (FeCO) of the locust hind leg elicit resistance reflexes in extensor and flexor tibiae motor neurons. The synaptic responses of the fast and slow extensor tibiae motor neurons (FETi and SETi, respectively) and the spike responses of SETi were analyzed with the use of the Wiener kernel white noise method to determine their response properties. The first-order Wiener kernels computed from soma recordings were essentially monophasic, or low passed, indicating that the motor neurons were primarily sensitive to the position of the tibia about the femorotibial joint. The responses of both extensor motor neurons had large nonlinear components. The second-order kernels of the synaptic responses of FETi and SETi had large on-diagonal peaks with two small off-diagonal valleys. That of SETi had an additional elongated valley on the diagonal, which was accompanied by two off-diagonal depolarizing peaks at a cutoff frequency of 58 Hz. These second-order components represent a half-wave rectification of the position-sensitive depolarizing response in FETi and SETi, and a delayed inhibitory input to SETi, indicating that both motor neurons were directionally sensitive. Model predictions of the responses of the motor neurons showed that the first-order (linear) characterization poorly predicted the actual responses of FETi and SETi to FeCO stimulation, whereas the addition of the second-order (nonlinear) term markedly improved the performance of the model. Simultaneous recordings from the soma and a neuropilar process of FETi showed that its synaptic responses to FeCO stimulation were phase delayed by about -30 degrees at 20 Hz, and reduced in amplitude by 30-40% when recorded in the soma. Similar configurations of the first and second-order kernels indicated that the primary process of FETi acted as a low-pass filter. Cross-correlation between a white noise stimulus and a unitized spike discharge of SETi again produced well-defined first- and second-order kernels that showed that the SETi spike response was also dependent on positional inputs. An elongated negative valley on the diagonal, characteristic of the second-order kernel of the synaptic response in SETi, was absent in the kernel from the spike component, suggesting that information is lost in the spike production process. The functional significance of these results is discussed in relation to the behavior of the locust.

Neural circuitry underlying linear representation of wind information in a nonspiking local interneuron of the cockroach

In the cercal system of the cockroach Periplaneta americana, primary sensory interneurons exhibiting a sharp directional sensitivity respond to wind in a linear manner whereas those exhibiting an omnidirectional sensitivity respond nonlinearly. For example, the wind-evoked response in an identifiable, nonspiking local interneuron, 101, which responds preferentially to wind from the left versus the right, is characterized exclusively by a differential first-order (linear) kernel. However, the slow potential response in a cercal giant interneuron, GI-1, is omnidirectional, and characterized by a second-order (nonlinear) kernel with an elongated depolarizing peak on the diagonal with two off-diagonal valleys. We here examined the neural circuitry underlying the linear and nonlinear representations of wind information by the deprivation of inputs from particular sets of cercal hair afferents. Electrical stimulation of the ipsilateral (related to the soma) cercal nerve elicited a depolarizing potential in 101, which was followed by delayed hyperpolarization. A continuous flow of 10(-4) M picrotoxin, which selectively blocked this delayed hyperpolarization, resulted in a significant change in the 101 response from linear to nonlinear. Because no frequency-doubling response was observed, the nonlinearity is due to signal compression (or rectification) that reflects the mechanical property of cercal afferents. This is consistent with the hypothesis that the linear representation in 101 is based on a subtraction process between two subsets of particular column hairs, whose best optimal directions are opposite to each other.

The identification of nonlinear biological systems: Volterra kernel approaches

Representation, identification, and modeling are investigated for nonlinear biomedical systems. We begin by considering the conditions under which a nonlinear system can be represented or accurately approximated by a Volterra series (or functional expansion). Next, we examine system identification through estimating the kernels in a Volterra functional expansion approximation for the system. A recent kernel estimation technique that has proved to be effective in a number of biomedical applications is investigated as to running time and demonstrated on both clean and noisy data records, then it is used to illustrate identification of cascades of alternating dynamic linear and static nonlinear systems, both single-input single-output and multivariable cascades. During the presentation, we critically examine some interesting biological applications of kernel estimation techniques.

The identification of nonlinear biological systems: Volterra kernel approaches

Representation, identification, and modeling are investigated for nonlinear biomedical systems. We begin by considering the conditions under which a nonlinear system can be represented or accurately approximated by a Volterra series (or functional expansion). Next, we examine system identification through estimating the kernels in a Volterra functional expansion approximation for the system. A recent kernel estimation technique that has proved to be effective in a number of biomedical applications is investigated as to running time and demonstrated on both clean and noisy data records, then it is used to illustrate identification of cascades of alternating dynamic linear and static nonlinear systems, both single-input single-output and multivariable cascades. During the presentation, we critically examine some interesting biological applications of kernel estimation techniques.

A simple spike train decoder inspired by the sampling theorem

Reconstructing a time-varying stimulus estimate from a spike train (Bialek's "decoding" of a spike train) has become an important way to study neural information processing. In this paper, we describe a simple method for reconstructing a time-varying current injection signal from the simulated spike train it produces. This technique extracts most of the information from the spike train, provided that the input signal is appropriately matched to the spike generator. To conceptualize this matching, we consider spikes as instantaneous "samples" of the somatic current. The Sampling Theorem is then applicable, and it suggests that the bandwidth of the injected signal not exceed half the spike generator's average firing rate. The average firing rate, in turn, depends on the amplitude range and DC bias of the injected signal. We hypothesize that nature faces similar problems and constraints when transmitting a time-varying waveform from the soma of one neuron to the dendrite of the postsynaptic cell.

Third-order reverse correlation analysis of muscle spindle primary afferent fiber responses to random muscle stretch

The response of primary muscle spindle afferent fibers to muscle stretch is nonlinear. Now spindle responses (trains of action potentials) to band-limited Gaussian white noise length perturbations of the gastrocnemius muscles (input signal) are described in cats. The input noise upper cutoff frequency was clearly above the frequency range of physiological length changes in cat hindleg muscles. The input-output relation was analyzed by means of peri-spike averages (PSAs), which could be shown to correspond to the kernels of Wiener's white noise approach to systems identification. The present approach (the reverse correlation analysis) was applied up to the third order. An experiment consisted of two recordings: one (the source recording) to determine PSAs and the other (the test recording) to provide an input signal for predicting responses. The predictions of different orders were compared with the actual neuronal response (the observation) of the test recording. Four different approximation procedures were developed to adapt prediction and observation and to determine weighting factors for the predictions of different orders. The approximations also yielded the value of the power density P of the input noise signal: at a variety of stimulus parameters, P from approximations had the same magnitude as P determined directly from the input signal amplitude spectrum. The prediction of a sequence of action potentials improved the higher the order of components. 37 of 42 action potentials of a test recording (the observation) could be confidently predicted from PSAs or kernels. Compared with the size of the linear first-order prediction curve, the relative sizes of the second and third-order prediction curves were: 1.0:0.47:0.26.

Response dynamics and receptive-field organization of catfish ganglion cells

Responses from catfish retinal ganglion cells were evoked by a spot or an annulus of light and were analyzed by a procedure identical to the one used previously to study catfish amacrine cells (Sakai H. M., and K.-I. Naka, 1992. Journal of Neurophysiology. 67:430-442.). In two-input white-noise experiments, a response evoked by simultaneous stimulation of the center and surround was decomposed into the components generated by the center and surround through a process of cross-correlation. The center and surround responses were also decomposed into their linear and nonlinear components so that the response dynamics of the linear and nonlinear components could be measured. We found that the concentric organization of the receptive field was determined by linear components, i.e., the first-order kernels generated by the center and surround were of opposite polarity. Both the center and surround generated second-order kernels with similar signatures, i.e., the second-order components formed a monotonic receptive field. The peak response time of the first- and second-order kernels from the surround was longer by approximately 20 ms than that of the center. Except for the DC potential present in the intracellular responses, almost identical first- and second-order kernels for the center and surround were obtained from both the intracellular response and spike discharges. Thus, information on concentric organization of a receptive field is translated into spike discharges with little loss of information. A train of spike discharges carries, simultaneously, at least four kinds of information: two linear and two nonlinear components, which originate in the receptive field center and the surround. A spike train is not a simple signaling device but is a carrier of complex and multiple signals. Victor, J. D., and R. M. Shapley (1979. Journal of General Physiology. 74:671-687.) discovered similarly that, in the cat retina, static second-order nonlinearity is encoded into spike trains. Results obtained in this study support the thesis that signals generated by the preganglionic cells are translated into spike discharges without major modification and that those signals can be recovered from the spike trains (Sakuranaga, M., Y. Ando, and K.-I. Naka. 1987. Journal of General Physiology. 90:229-259.; Korenberg, M. J., H. M. Sakai, and K.-I. Naka. 1989. Journal of Neurophysiology. 61:1110-1120.). Current injection studies have shown that such signal transmission is possible (Sakai, H. M., and K.-I. Naka, 1988a. Journal of Neurophysiology. 60:1549-1567.; 1990. Journal of Neurophysiology. 63:105-119.).

Contrast gain control in the lower vertebrate retinas

Control of contrast sensitivity was studied in two kinds of retina, that of the channel catfish and that of the kissing gourami. The former preparation is dominantly monochromatic and the latter is bichromatic. Various stimuli were used, namely a large field of light, a spot-annulus configuration and two overlapping stimuli of red and green. Recordings were made from horizontal, amacrine, and ganglion cells and the results were analyzed by means of Wiener's theory, in which the kernels are the contrast (incremental) sensitivity. Modulation responses from horizontal cells are linear, in that the waveform and amplitude of the first-order kernels are independent of the depth of modulation. In the N (sustained) amacrine and ganglion cells, contrast sensitivity was low for a large modulation input and was high for a small modulation input, providing an example of contrast gain control. In most of the cells, the contrast gain control did not affect the dynamics of the response because the waveform of the first-order kernels remained unchanged when the contrast sensitivity increased more than fivefold. The signature of the second-order kernels also remained unchanged over a wide range of modulation. The increase in the contrast sensitivity for the second-order component, as defined by the amplitude of the kernels, was much larger than for the first-order component. This observation suggests that the contrast gain control proceeded the generation of the second-order nonlinearity. An analysis of a cascade of the Wiener type shows that the control of contrast sensitivity in the proximal retinal cells could be modeled by assuming the presence of a simple (static) saturation nonlinearity. Such a nonlinearity must exist somewhere between the horizontal cells and the amacrine cells. The functional implications of the contrast gain control are as follows: (a) neurons in the proximal retina exhibit greater sensitivity to input of lower contrast; (b) saturation of a neuronal response can be prevented because of the lower sensitivity for an input with large contrast, and (c) over a large range of modulation depths, the amplitude of the response remains approximately constant.

Reverse correlation analysis of the stretch response of primary muscle spindle afferent fibers

The nonlinear responses of deefferented primary muscle spindle afferent fibers to muscle stretching consisted of a train of action potentials which was analyzed when random changes in muscle length (band-limited gaussian white noise) were applied in cats. The upper cutoff frequency of the applied noise (the source stimulus) was varied between 1.6 and 570 Hz; the amplitude of the random input was varied between 0.002 and 1.2 mm. In a previous report the reverse correlation of 1st and 2nd order was studied for its ability to analyze data of a continuous input signal and pulsatile events in the output. Computations of the Wiener kernels h1 and h2 or their equivalents, the perispike averages of the 1st and 2nd order, were computed from the random stretch responses of muscle-spindle afferents. Then the 1st- and the 2nd-order predictions and the summation of both to random muscle stretch was estimated. A general finding was that the 1st-order component was approximately 10 times that of the 2nd-order component, when both were combined in approximation procedures to give the closest prediction of observed responses to random test stimuli. The approximation was poor when the source stimulus was less than 0.03 mm and improved when it was greater. With the increase in the upper cutoff frequency of the random source input, the approximation worsened continuously. Predictions to ramp-and-hold stimuli were computed, as well as responses to random stimulation. Limiting the upper cutoff frequency did not diminish the value of the techniques applied.

Filter characteristics of cercal afferents in the cockroach

The response dynamics of cercal afferents in the cockroach. Periplaneta americana, were determined by means of a cross-correlation technique using a Gaussian white noise modulation of wind as a stimulus. The white noise stimulus could evoke sustained firing activity in most of the afferents examined (Fig. 1). The spike discharges were unitized and then cross-correlated with the stimulus to compute 1st- and 2nd-order Weiner kernels. The 1st-order kernels from a total of 28 afferents were biphasic and closely matched the time differential of a pulse (Figs. 1, 3 and 4). The amplitude and waveform of the kernels depended on the stimulus angle in such a way that the kernels were the mirror image of those on the polar opposite side (Figs. 2 and 3). The 2nd-order kernels were also differential. They had 2 diagonal peaks and 2 off-diagonal valleys in a 2-dimensional plot with 2 time axes (Figs. 1, 5 and 6). This 4-eye configuration was basically invariant irrespective of the stimulus angle, although the kernels varied in amplitude when the stimulus angle was changed. The time between the peak and a following trough of the 1st-order kernel was constant and had a mean of 4.6 +/- 0.1 ms, whereas the time between 2 diagonal peaks of the 2nd-order kernels was 4.7 +/- 0.1 ms (Figs. 4 and 6), suggesting that wind receptors (filiform sensilla) on cerci act as a band-pass filter with a peak frequency of about 106 Hz. The peak time, however, varies from 2.3 to 6.9 ms in both kernels, which may reflect the spatial distribution of the corresponding hairs on the cercus. The summation of the 1st- (linear) and 2nd-order (nonlinear) models precisely predicted the timing of the spike firing (Fig. 8). Thus, these 2 lower-order kernels can totally characterize the response dynamics of the wind receptors. The nonlinear response explains the directional sensitivity of the sensory neurons, while the differentiating 1st-order kernel explains the velocity sensitivity of the neurons. The nonlinearity is a signal compression in which one of the diagonal peaks of the 2nd-order kernel always offsets the downward phase of the 1st-order kernel (Fig. 7) and obviously represents a half-wave rectification property of the wind receptors that are excited by hair movement in only one direction and inhibited by hair movement in the polar opposite direction.

Dissection of a nonlinear cascade model for sensory encoding

Action potential encoding in the cockroach tactile spine neuron may be treated as a single-input, single-output dynamic nonlinear process, where the input is the electric current flowing across the neuronal membrane and the output is the resultant train of action potentials. The nonlinear behavior of the system may be characterized by a functional expansion method which efficiently and accurately yields similar kernels to the Wiener method. A simple nonlinear cascade consisting of sequential dynamic linear, static nonlinear, and dynamic linear components was identified and gives a good approximation to the response of the neuron to random stimulation. Next, we attempted to study the components of the cascade by the use of a drug, phentolamine, which selectively modifies the dynamic behavior of the encoder. Application of phentolamine to the neuron caused a significant change in the first dynamic linear component of the cascade without affecting the other components. The change was much larger than the variability between results obtained from individual animals. This finding has implications for the biophysical processes which are involved in the components of the cascade.

The messages in optic nerve fibers and their interpretation

Spike discharges are the principal carriers of information in the nervous system. Although both the ionic and the molecular mechanisms of spike generation have been studied extensively, the methods for analyzing a spike train that are currently employed have not changed much from those in use 20 years ago. There is an apparent need for a refinement of the methods used to analyze spike trains. We present here a summary of our recent results of an analysis of spike trains from retinal ganglion cells that is based on Wiener's theory of non-linear analysis or white-noise analysis. We found that spike trains carry, at least to a second-order approximation, as much information as is carried by the ganglion cell's postsynaptic potential (PSP). There is no loss of information when an analog signal, PSP, is converted into a point process, namely, spike discharges. It is indeed possible to predict the cell's PSP from a spike train. This finding has two important implications. First, the neuron network in the retina produces a PSP, the dynamics of which are optimal for triggering a spike discharge, or conversely, the spike-generation mechanism is optimized to match the dynamics of the network. The external stimulus that is optimal for production of a ganglion-cell discharge is represented as the cell's PSP. Second, there is structure encoded within the spike train; information on a second-order non-linearity is encoded by the relative timing of two consecutive spike discharges. Coding of non-linearity into a spike train is an efficient means of signal compression and is an important aspect of neurophysiology.

White noise analysis of graded response in a wind-sensitive, nonspiking interneuron of the cockroach

1. A novel approach using a Gaussian white noise as stimulus is described which allowed quantitative analysis of neuronal responses in the cercal system of the cockroach, Periplaneta americana. Cerci were stimulated by air displacement which was modulated by a sinusoidal and a white noise signal. During the stimulation, intracellular recordings were made from a uniquely identifiable, nonspiking, local interneuron which locates within the terminal abdominal ganglion. The white noise stimulation was cross-correlated with the evoked response to compute first- and second-order kernels that could define the cell's response dynamics. 2. The interneuron, cell 101, has an exceptionally large transverse neurite that connects two asymmetrical dendritic arborizations located on both sides of the ganglion. 3. The first-order Wiener kernels in cell 101 were biphasic (differentiating). The waveforms of the kernels produced by the ipsilateral and contralateral stimulations were roughly mirror images of each other: the kernels produced by wind stimuli on the side ipsilateral to the cell body of the interneuron are initially depolarized and then hyperpolarized, whereas those on the other side are initially hyperpolarized. The polarity reversal occurred along the midline of the animal's body, and no well-defined kernel was produced by a stimulus directed head on or from the tail. 4. Mean square error (MSE) between the actual response and the model prediction suggests that the linear component in cell 101 comprises half of the cell's total response (MSEs for the linear models were about 50% at preferred directions), whereas the second-order, non-linear component is insignificant. The linear component of the wind-evoked response was bandpass with the preferred frequency of 70-90 Hz. 5. Accounting for a noise, we reasonably assumed that at high frequencies the graded response in cell 101 is linearly related to a modulation of the air displacement and sensitive to the rate of change of the signal (i.e., wind velocity) and the direction of its source. It is suggested that the dynamics of the first-order kernel simply reflect the dynamics of sensory receptors that respond linearly to wind stimulation.

The identification of nonlinear biological systems: Wiener kernel approaches

Detection, representation, and identification of nonlinearities in biological systems are considered. We begin by briefly but critically examining a well-known test of system nonlinearity, and point out that this test cannot be used to prove that a system is linear. We then concentrate on the representation of nonlinear systems by Wiener's orthogonal functional series, discussing its advantages, limitations, and biological applications. System identification through estimating the kernels in the functional series is considered in detail. An efficient time-domain method of correcting for coloring in inputs is examined and shown to result in significantly improved kernel estimates in a biologically realistic system.

A nonlinear cascade model for action potential encoding in an insect sensory neuron

Action potential encoding in the cockroach tactile spine neuron can be represented as a single-input single-output nonlinear dynamic process. We have used a new functional expansion method to characterize the nonlinear behavior of the neural encoder. This method, which yields similar kernels to the Wiener method, is more accurate than the latter and is efficient enough to obtain reasonable kernels in less than 15 min using a personal computer. The input stimulus was band-limited white Gaussian noise and the output consisted of the resulting train of action potentials, which were unitized to give binary values. The kernels and the system input-output signals were used to identify a model for encoding comprising a cascade of dynamic linear, static nonlinear, and dynamic linear components. The two dynamic linear components had repeatable and distinctive forms with the first being low-pass and the second being high-pass. The static nonlinearity was fitted with a fifth-order polynomial function over several input amplitude ranges and had the form of a half-wave rectifier. The complete model gave a good approximation to the output of the neuron when both were subjected to the same novel white noise input signal.

Dynamic relationship between the slow potential and spikes in cockroach ocellar neurons

The relationship between the slow potential and spikes of second-order ocellar neurons of the cockroach, Periplaneta americana, was studied. The stimulus was a sinusoidally modulated light with various mean illuminances. A solitary spike was generated at the depolarizing phase of the modulation response. Analysis of the relationship between the amplitude/frequency of voltage modulation and the rate of spike generation showed that (a) the spike initiation process was bandpass at approximately 0.5-5 Hz, (b) the process contained a dynamic linearity and a static nonlinearity, and (c) the spike threshold at optimal frequencies (0.5-5 Hz) remained unchanged over a mean illuminance range of 3.6 log units, whereas (d) the spike threshold at frequencies of less than 0.5 Hz was lower at a dimmer mean illuminance. The voltage noise in the response was larger and the mean membrane potential level was more positive at a dimmer mean illuminance. Steady or noise current injection during sinusoidal light stimulation showed that (a) the decrease in the spike threshold at a dimmer mean illuminance was due to the increase in the noise variance: the noise had facilitatory effects on the spike initiation; and (b) the change in the mean potential level had little effect on the spike threshold. We conclude that fundamental signal modifications occur during the spike initiation in the cockroach ocellar neuron, a finding that differs from the spike initiation process in other visual systems, including Limulus eye and vertebrate retina, in which it is presumed that little signal modification occurs at the analog-to-digital conversion process.

White-noise analysis in visual neuroscience

In 1827, plant biologist Robert Brown discovered what is known as Brownian motion, a class of chaos. Formal derivative of Brownian motion is Gaussian white-noise. In 1938, Norbert Wiener proposed to use the Gaussian white-noise as an input probe to identify a system by a series of orthogonal functionals known as the Wiener G-functionals. White-noise analysis is uniquely suited for studying the response dynamics of retinal neurons because (1) white-noise light stimulus is a modulation around a mean luminance, as are the natural photic inputs, and it is a highly efficient input; and (2) the analysis defines the response dynamics and can be extended to spike trains, the final output of the retina. Demonstrated here are typical examples and results from applications of white-noise analysis to a visual system.

Generation and transformation of second-order nonlinearity in catfish retina

A large part of the response from catfish retinal neurons evoked by a white-noise modulated light stimulus is reconstructed by the linear and the second-order nonlinear components, which shows that the first- and second-order kernels represent the major response characteristics. In catfish retina, amacrine cells are classified as type-C and type-N cells. Type-C cells produce a stable and stereotyped second-order kernel that can be reproduced by squaring an underdamped first-order kernel. This is a linear filter followed by a static nonlinearity and is modeled by a cascade of the Wiener structure. A second-order kernel from the other class of amacrine cells, type-N cells, is reproduced by a simple linear filtering of type-C cell response. This is a static nonlinearity sandwiched between two linear filters and is modelled by a cascade of the Korenberg structure. These findings may greatly simplify future attempts to reconstruct retinal circuitry and may give some insight into the process of complex signal processing in the inner part of the vertebrate retina.