Abstract
Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed from spikes, has received less attention, especially for complex stimuli 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 randomly-moving discs. We constructed nonlinear (kernelized and neural network) decoders that improved significantly over linear results. An important contribution to this was the ability of nonlinear decoders to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous-like 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. We propose a general principle by which downstream circuitry could discriminate between spontaneous and stimulus-driven activity based solely on higher-order statistical structure in the incoming spike trains.
Highlights
Decoding plays a central role in our efforts to understand the neural code [1,2,3,4]
We examine nonlinear decoding using kernel ridge regression (KRR [23]) and deep learning [24], which provide a substantial increase in performance over linear decoding, and isolate spike train statistics that the nonlinear decoders are making use of
Could nonlinear decoding improve on these results? We considered two nonlinear regression methods that can tractably be applied to our data: kernel ridge regression (KRR) and regression using deep neural networks
Summary
Decoding plays a central role in our efforts to understand the neural code [1,2,3,4]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have