https://www.ncbi.nlm.nih.gov/pubmed/29746463
Nonlinear decoding of a complex movie from the mammalian retina.
Author information
- 1
- Institute of Science and Technology Austria, Klosterneuburg, Austria.
- 2
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012 Paris, France.
- 3
- Max Planck Institute for Intelligent Systems, Tübingen, Germany.
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.
- PMID:
- 29746463
- PMCID:
- PMC5944913
- DOI:
- 10.1371/journal.pcbi.1006057
쥐의 망막에 빛의 신호(complex movie..)를 주면, 망막에서 전기적 신호로 변한다.
전기적 신호(neural incoming spikes) 를 다시 decoding 하여 분석했다. (kernelized and neural network decoders)
Author summary
Neurons in the retina transform patterns of incoming light into sequences of neural spikes.
We recorded from *100 neurons in the rat retina while it was stimulated with a complex
movie. Using machine learning regression methods, we fit decoders to reconstruct the
movie shown from the retinal output. We demonstrated that retinal code can only be read
out with a low error if decoders make use of correlations between successive spikes emitted
by individual neurons. These correlations can be used to ignore spontaneous spiking that
would, otherwise, cause even the best linear decoders to “hallucinate” nonexistent stimuli.
This work represents the first high resolution single-trial full movie reconstruction and
suggests a new paradigm for separating spontaneous from stimulus-driven neural activity.
201806 PLos Nonlinear decoding of a complex movie from the mammalian retina.pdf
