Extreme Synergy in a Retinal Code: Spatiotemporal Correlations Enable Rapid Image Reconstruction

TL;DR

This study demonstrates that spatiotemporal correlations among retinal neurons, modulated by oscillatory inputs, significantly enhance rapid image reconstruction by extracting distributed intensity information, reducing spike requirements.

Contribution

It introduces a novel non-linear encoding strategy using PCA on pairwise correlations to improve visual signal decoding from retinal spike trains.

Findings

Correlations improve signal/noise ratio in retinal responses.

Oscillatory modulation links neurons to encode intensity.

Rapid reconstruction achieved with short, 25 ms spike trains.

Abstract

Over the brief time intervals available for processing retinal output, roughly 50 to 300 msec, the number of extra spikes generated by individual ganglion cells can be quite variable. Here, computer-generated spike trains were used to investigate how signal/noise might be improved by utilizing spatiotemporal correlations among retinal neurons responding to large, contiguous stimuli. Realistic correlations were produced by modulating the instantaneous firing probabilities of all stimulated neurons by a common oscillatory input whose amplitude and temporal structure were consistent with experimentally measured field potentials and correlograms. Whereas previous studies have typically measured synergy between pairs of ganglion cells examined one at a time, or alternatively have employed optimized linear filters to decode activity across larger populations, the present study investigated a distributed, non-linear encoding strategy by using Principal Components Analysis (PCA) to reconstruct simple visual stimuli from up to one million oscillatory pairwise correlations extracted on single trials from massively-parallel spike trains as short as 25 msec in duration. By integrating signals across retinal neighborhoods commensurate in size to classical antagonistic surrounds, the first principal component of the pairwise correlation matrix yielded dramatic improvements in signal/noise without sacrificing fine spatial detail. These results demonstrate how local intensity information can distributed across hundreds of neurons linked by a common, stimulus-dependent oscillatory modulation, a strategy that might have evolved to minimize the number of spikes required to support rapid image reconstruction.