Modeling the impact of common noise inputs on the network activity of retinal ganglion cells

Journal of Computational Neuroscience - Tập 33 - Trang 97-121 - 2011
Michael Vidne1, Yashar Ahmadian2, Jonathon Shlens3, Jonathan W. Pillow4, Jayant Kulkarni5, Alan M. Litke6, E. J. Chichilnisky3, Eero Simoncelli7, Liam Paninski2
1Department of Applied Physics & Applied Mathematics, Center for Theoretical Neuroscience, Columbia University, New York, USA
2Center for Theoretical Neuroscience, Columbia University, New York, USA
3The Salk Institute for Biological Studies, La Jolla, USA
4Center for Perceptual Systems, The University of Texas at Austin, Austin, USA
5Cold Spring Harbor Laboratory, Cold Spring Harbor, USA
6Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, USA
7Howard Hughes Medical Institute, Center for Neural Science, and Courant Institute of Mathematical Sciences, New York University, New York, USA

Tóm tắt

Synchronized spontaneous firing among retinal ganglion cells (RGCs), on timescales faster than visual responses, has been reported in many studies. Two candidate mechanisms of synchronized firing include direct coupling and shared noisy inputs. In neighboring parasol cells of primate retina, which exhibit rapid synchronized firing that has been studied extensively, recent experimental work indicates that direct electrical or synaptic coupling is weak, but shared synaptic input in the absence of modulated stimuli is strong. However, previous modeling efforts have not accounted for this aspect of firing in the parasol cell population. Here we develop a new model that incorporates the effects of common noise, and apply it to analyze the light responses and synchronized firing of a large, densely-sampled network of over 250 simultaneously recorded parasol cells. We use a generalized linear model in which the spike rate in each cell is determined by the linear combination of the spatio-temporally filtered visual input, the temporally filtered prior spikes of that cell, and unobserved sources representing common noise. The model accurately captures the statistical structure of the spike trains and the encoding of the visual stimulus, without the direct coupling assumption present in previous modeling work. Finally, we examined the problem of decoding the visual stimulus from the spike train given the estimated parameters. The common-noise model produces Bayesian decoding performance as accurate as that of a model with direct coupling, but with significantly more robustness to spike timing perturbations.

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