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Over the brief time intervals available for processing retinal output,roughly 50 to 300 msec, the number of extra spikes generated by individualganglion cells can be quite variable. Here, computer-generated spike trainswere used to investigate how signal/noise might be improved by utilizingspatiotemporal correlations among retinal neurons responding to large,contiguous stimuli. Realistic correlations were produced by modulating theinstantaneous firing probabilities of all stimulated neurons by a commonoscillatory input whose amplitude and temporal structure were consistent withexperimentally measured field potentials and correlograms. Whereas previousstudies have typically measured synergy between pairs of ganglion cellsexamined one at a time, or alternatively have employed optimized linear filtersto decode activity across larger populations, the present study investigated adistributed, non-linear encoding strategy by using Principal ComponentsAnalysis (PCA) to reconstruct simple visual stimuli from up to one millionoscillatory pairwise correlations extracted on single trials frommassively-parallel spike trains as short as 25 msec in duration. By integratingsignals across retinal neighborhoods commensurate in size to classicalantagonistic surrounds, the first principal component of the pairwisecorrelation matrix yielded dramatic improvements in signal/noise withoutsacrificing fine spatial detail. These results demonstrate how local intensityinformation can distributed across hundreds of neurons linked by a common,stimulus-dependent oscillatory modulation, a strategy that might have evolvedto minimize the number of spikes required to support rapid imagereconstruction.

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