Nonlinear decoding of a complex movie from the mammalian retina

TL;DR

This study demonstrates that nonlinear decoding methods significantly improve the reconstruction of complex visual stimuli from retinal neural activity by leveraging spike train temporal structure, revealing insights into neural discrimination mechanisms.

Contribution

Introduces nonlinear (kernelized) decoders for retinal data that outperform linear methods by utilizing spike train temporal structure for stimulus reconstruction.

Findings

Nonlinear decoders outperform linear ones in movie reconstruction.

Spike train temporal structure is crucial for decoding accuracy.

Neural responses can be distinguished based on higher-order spike train statistics.

Abstract

Retinal circuitry transforms spatiotemporal patterns of light into spiking activity of ganglion cells, which provide the sole visual input to the brain. Recent advances have led to a detailed characterization of retinal activity and stimulus encoding by large neural populations. The inverse problem of decoding, where the stimulus is reconstructed from spikes, has received less attention, in particular for complex input movies that should be reconstructed "pixel-by-pixel". We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small discs executing mutually-avoiding random motions. We constructed nonlinear (kernelized) decoders that improved significantly over linear decoding results, mostly due to their ability to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous or network activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. Our results suggest a general paradigm in which downstream neural circuitry could discriminate between spontaneous and stimulus-driven activity on the basis of higher-order statistical structure intrinsic to the incoming spike trains.