E. J. Chichilnisky

Spatio-temporal correlations and visual signalling in a complete neuronal population

Statistical dependencies in the responses of sensory neurons govern both the amount of stimulus information conveyed and the means by which downstream neurons can extract it. Although a variety of measurements indicate the existence of such dependencies, their origin and importance for neural coding are poorly understood. Here we analyse the functional significance of correlated firing in a complete population of macaque parasol retinal ganglion cells using a model of multi-neuron spike responses. The model, with parameters fit directly to physiological data, simultaneously captures both the stimulus dependence and detailed spatio-temporal correlations in population responses, and provides two insights into the structure of the neural code. First, neural encoding at the population level is less noisy than one would expect from the variability of individual neurons: spike times are more precise, and can be predicted more accurately when the spiking of neighbouring neurons is taken into account. Second, correlations provide additional sensory information: optimal, model-based decoding that exploits the response correlation structure extracts 20% more information about the visual scene than decoding under the assumption of independence, and preserves 40% more visual information than optimal linear decoding. This model-based approach reveals the role of correlated activity in the retinal coding of visual stimuli, and provides a general framework for understanding the importance of correlated activity in populations of neurons.

A simple white noise analysis of neuronal light responses

A white noise technique is presented for estimating the response properties of spiking visual system neurons. The technique is simple, robust, efficient and well suited to simultaneous recordings from multiple neurons. It provides a complete and easily interpretable model of light responses even for neurons that display a common form of response nonlinearity that precludes classical linear systems analysis. A theoretical justification of the technique is presented that relies only on elementary linear algebra and statistics. Implementation is described with examples. The technique and the underlying model of neural responses are validated using recordings from retinal ganglion cells, and in principle are applicable to other neurons. Advantages and disadvantages of the technique relative to classical approaches are discussed.

The Structure of Multi-Neuron Firing Patterns in Primate Retina

Current understanding of many neural circuits is limited by our ability to explore the vast number of potential interactions between different cells. We present a new approach that dramatically reduces the complexity of this problem. Large-scale multi-electrode recordings were used to measure electrical activity in nearly complete, regularly spaced mosaics of several hundred ON and OFF parasol retinal ganglion cells in macaque monkey retina. Parasol cells exhibited substantial pairwise correlations, as has been observed in other species, indicating functional connectivity. However, pairwise measurements alone are insufficient to determine the prevalence of multi-neuron firing patterns, which would be predicted from widely diverging common inputs and have been hypothesized to convey distinct visual messages to the brain. The number of possible multi-neuron firing patterns is far too large to study exhaustively, but this problem may be circumvented if two simple rules of connectivity can be established: (1) multi-cell firing patterns arise from multiple pairwise interactions, and (2) interactions are limited to adjacent cells in the mosaic. Using maximum entropy methods from statistical mechanics, we show that pairwise and adjacent interactions accurately accounted for the structure and prevalence of multi-neuron firing patterns, explaining ∼98% of the departures from statistical independence in parasol cells and ∼99% of the departures that were reproducible in repeated measurements. This approach provides a way to define limits on the complexity of network interactions and thus may be relevant for probing the function of many neural circuits.

Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model

Sensory encoding in spiking neurons depends on both the integration of sensory inputs and the intrinsic dynamics and variability of spike generation. We show that the stimulus selectivity, reliability, and timing precision of primate retinal ganglion cell (RGC) light responses can be reproduced accurately with a simple model consisting of a leaky integrate-and-fire spike generator driven by a linearly filtered stimulus, a postspike current, and a Gaussian noise current. We fit model parameters for individual RGCs by maximizing the likelihood of observed spike responses to a stochastic visual stimulus. Although compact, the fitted model predicts the detailed time structure of responses to novel stimuli, accurately capturing the interaction between the spiking history and sensory stimulus selectivity. The model also accounts for the variability in responses to repeated stimuli, even when fit to data from a single (nonrepeating) stimulus sequence. Finally, the model can be used to derive an explicit, maximum-likelihood decoding rule for neural spike trains, thus providing a tool for assessing the limitations that spiking variability imposes on sensory performance.

Adaptation to Temporal Contrast in Primate and Salamander Retina

Visual adaptation to temporal contrast (intensity modulation of a spatially uniform, randomly flickering stimulus) was examined in simultaneously recorded ensembles of retinal ganglion cells (RGCs) in tiger salamander and macaque monkey retina. Slow contrast adaptation similar to that recently discovered in salamander and rabbit retina was observed in monkey retina. A novel method was developed to quantify the effect of temporal contrast on steady-state sensitivity and kinetics of light responses, separately from nonlinearities that would otherwise significantly contaminate estimates of sensitivity. Increases in stimulus contrast progressively and reversibly attenuated and sped light responses in both salamander and monkey RGCs, indicating that a portion of the contrast adaptation observed in visual cortex originates in the retina. The effect of adaptation on sensitivity and kinetics differed in simultaneously recorded populations of ON and OFF cells. In salamander, adaptation affected the sensitivity of OFF cells more than ON cells. In monkey, adaptation affected the sensitivity of ON cells more than OFF cells. In both species, adaptation sped the light responses of OFF cells more than ON cells. Functionally defined subclasses of ON and OFF cells also exhibited asymmetric adaptation. These findings indicate that contrast adaptation differs in parallel retinal circuits that convey distinct visual signals to the brain.

What does the eye tell the brain?: Development of a system for the large-scale recording of retinal output activity

A multielectrode array system has been developed to study how the retina processes and encodes visual images. This system can simultaneously record the extracellular electrical activity from hundreds of retinal output neurons as a dynamic visual image is focused on the input neurons. The retinal output signals detected can be correlated with the visual input to study the neural code used by the eye to send information about the visual world to the brain. The system consists of the following components: (1) a 32 /spl times/ 16 rectangular array of 512 planar microelectrodes with a sensitive area of 1.7 square mm. The electrode spacing is 60 microns and the electrode diameter is 5 microns. (Hexagonal arrays with 519 electrodes are under development); (2) eight 64-channel custom-designed integrated circuits to platinize the electrodes and AC couple the signals; (3) eight 64-channel integrated circuits to amplify, band-pass filter and analog multiplex the signals; (4) a data acquisition system; and (5) data processing software. This paper will describe the design of the system, the experimental and data analysis techniques, and some first results with live retina. The system is based on techniques and expertise acquired in the development of silicon microstrip detectors for high energy physics experiments.

Functional connectivity in the retina at the resolution of photoreceptors.

To understand a neural circuit requires knowledge of its connectivity. Here we report measurements of functional connectivity between the input and ouput layers of the macaque retina at single-cell resolution and the implications of these for colour vision. Multi-electrode technology was used to record simultaneously from complete populations of the retinal ganglion cell types (midget, parasol and small bistratified) that transmit high-resolution visual signals to the brain. Fine-grained visual stimulation was used to identify the location, type and strength of the functional input of each cone photoreceptor to each ganglion cell. The populations of ON and OFF midget and parasol cells each sampled the complete population of long- and middle-wavelength-sensitive cones. However, only OFF midget cells frequently received strong input from short-wavelength-sensitive cones. ON and OFF midget cells showed a small non-random tendency to selectively sample from either long- or middle-wavelength-sensitive cones to a degree not explained by clumping in the cone mosaic. These measurements reveal computations in a neural circuit at the elementary resolution of individual neurons.

High-resolution electrical stimulation of primate retina for epiretinal implant design

The development of retinal implants for the blind depends crucially on understanding how neurons in the retina respond to electrical stimulation. This study used multielectrode arrays to stimulate ganglion cells in the peripheral macaque retina, which is very similar to the human retina. Analysis was restricted to parasol cells, which form one of the major high-resolution visual pathways in primates. Individual cells were characterized using visual stimuli, and subsequently targeted for electrical stimulation using electrodes 9–15 μm in diameter. Results were accumulated across 16 ON and 9 OFF parasol cells. At threshold, all cells responded to biphasic electrical pulses 0.05–0.1 ms in duration by firing a single spike with latency lower than 0.35 ms. The average threshold charge density was 0.050 ± 0.005 mC/cm2, significantly below established safety limits for platinum electrodes. ON and OFF ganglion cells were stimulated with similar efficacy. Repetitive stimulation elicited spikes within a 0.1 ms time window, indicating that the high temporal precision necessary for spike-by-spike stimulation can be achieved in primate retina. Spatial analysis of observed thresholds suggests that electrical activation occurred near the axon hillock, and that dendrites contributed little. Finally, stimulation of a single parasol cell produced little or no activation of other cells in the ON and OFF parasol cell mosaics. The low-threshold, temporally precise, and spatially specific responses hold promise for the application of high-density arrays of small electrodes in epiretinal implants.

The structure of large-scale synchronized firing in primate retina.

Synchronized firing among neurons has been proposed to constitute an elementary aspect of the neural code in sensory and motor systems. However, it remains unclear how synchronized firing affects the large-scale patterns of activity and redundancy of visual signals in a complete population of neurons. We recorded simultaneously from hundreds of retinal ganglion cells in primate retina, and examined synchronized firing in completely sampled populations of ∼50–100 ON-parasol cells, which form a major projection to the magnocellular layers of the lateral geniculate nucleus. Synchronized firing in pairs of cells was a subset of a much larger pattern of activity that exhibited local, isotropic spatial properties. However, a simple model based solely on interactions between adjacent cells reproduced 99% of the spatial structure and scale of synchronized firing. No more than 20% of the variability in firing of an individual cell was predictable from the activity of its neighbors. These results held both for spontaneous firing and in the presence of independent visual modulation of the firing of each cell. In sum, large-scale synchronized firing in the entire population of ON-parasol cells appears to reflect simple neighbor interactions, rather than a unique visual signal or a highly redundant coding scheme.

Receptive-field microstructure of blue-yellow ganglion cells in primate retina

We examined the functional microcircuitry of cone inputs to blue-ON/yellow-OFF (BY) ganglion cells in the macaque retina using multielectrode recording. BY cells were identified by their ON responses to blue light and OFF responses to red or green light. Cone-isolating stimulation indicated that ON responses originated in short (S) wavelength-sensitive cones, whereas OFF responses originated in both long (L) and middle (M) wavelength-sensitive cones. Stimulation with fine spatial patterns revealed locations of individual S cones in BY cell receptive fields. Neighboring BY cells received common but unequal inputs from one or more S cones. Inputs from individual S cones differed in strength, indicating different synaptic weights, and summed approximately linearly to control BY cell firing.