Neural Coding of Natural Stimuli: Information at Sub-Millisecond Resolution

Ilya Nemenman,William Bialek,Rob R De Ruyter Van Steveninck,Geoffrey D Lewen,Karl J Friston

doi:10.1371/journal.pcbi.1000025

Abstract

Sensory information about the outside world is encoded by neurons in sequences of discrete, identical pulses termed action potentials or spikes. There is persistent controversy about the extent to which the precise timing of these spikes is relevant to the function of the brain. We revisit this issue, using the motion-sensitive neurons of the fly visual system as a test case. Our experimental methods allow us to deliver more nearly natural visual stimuli, comparable to those which flies encounter in free, acrobatic flight. New mathematical methods allow us to draw more reliable conclusions about the information content of neural responses even when the set of possible responses is very large. We find that significant amounts of visual information are represented by details of the spike train at millisecond and sub-millisecond precision, even though the sensory input has a correlation time of ∼55 ms; different patterns of spike timing represent distinct motion trajectories, and the absolute timing of spikes points to particular features of these trajectories with high precision. Finally, the efficiency of our entropy estimator makes it possible to uncover features of neural coding relevant for natural visual stimuli: first, the system's information transmission rate varies with natural fluctuations in light intensity, resulting from varying cloud cover, such that marginal increases in information rate thus occur even when the individual photoreceptors are counting on the order of one million photons per second. Secondly, we see that the system exploits the relatively slow dynamics of the stimulus to remove coding redundancy and so generate a more efficient neural code.

Highlights

Throughout the brain, information is represented by discrete electrical pulses termed action potentials or ‘spikes’ [1]
For decades there has been controversy about the extent to which the precise timing of these spikes is significant: Should we think of each spike arrival time as having meaning down to millisecond precision [2,3,4,5], or does the brain only keep track of the number of spikes occurring in much larger windows of time? Is precise timing relevant only in response to rapidly varying sensory stimuli, as in the auditory system [6], or can the brain construct specific patterns of spikes with a time resolution much smaller than the time scales of the sensory and motor signals that these patterns represent [3,7]? Here we address these issues using the motion-sensitive neurons of the fly visual system as a model [8]
We report a number of unexpected, striking observations about the structure of the neural code in this system: First, the timing of spikes is important with a precision roughly two orders of magnitude greater than the temporal dynamics of the stimulus

Summary

Introduction

Throughout the brain, information is represented by discrete electrical pulses termed action potentials or ‘spikes’ [1]. Is precise timing relevant only in response to rapidly varying sensory stimuli, as in the auditory system [6], or can the brain construct specific patterns of spikes with a time resolution much smaller than the time scales of the sensory and motor signals that these patterns represent [3,7]? We find that as we improve our time resolution for the analysis of spike trains from 2 ms down to a fraction of a millisecond we reveal nearly 30% more information about the trajectory of visual motion. The natural stimuli used in our experiments have essentially no power above 30 Hz, so that the precision of spike timing is not a necessary correlate of the stimulus bandwidth; instead the different patterns of precise spike timing represent subtly different trajectories chosen out of the stimulus ensemble. Despite the long correlation times of the sensory stimulus, segments of the neural response separated by ,30 ms provide essentially independent information, suggesting that the neural code in this system achieves decorrelation [13,14] in the time domain, thereby enhancing the efficiency of the code on time scales relevant to behavior [15]

Methods

Results

Conclusion