Monte Carlo methods for localization of cones given multielectrode retinal ganglion cell recordings.
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It has recently become possible to identify cone photoreceptors in primate retina from multi-electrode recordings of ganglion cell spiking driven by visual stimuli of sufficiently high spatial resolution. In this paper we present a statistical approach to the problem of identifying the number, locations, and color types of the cones observed in this type of experiment. We develop an adaptive Markov Chain Monte Carlo (MCMC) method that explores the space of cone configurations, using a Linear-Nonlinear-Poisson (LNP) encoding model of ganglion cell spiking output, while analytically integrating out the functional weights between cones and ganglion cells. This method provides information about our posterior certainty about the inferred cone properties, and additionally leads to improvements in both the speed and quality of the inferred cone maps, compared to earlier "greedy" computational approaches.
SubjectRetinal Ganglion Cells
Monte Carlo Method
Retinal Cone Photoreceptor Cells
Published Version (Please cite this version)10.3109/0954898X.2012.740140
Publication InfoField, Greg; Sadeghi, K; Gauthier, JL; Greschner, M; Agne, M; Chichilnisky, EJ; & Paninski, L (2013). Monte Carlo methods for localization of cones given multielectrode retinal ganglion cell recordings. Network (Bristol, England), 24(1). pp. 27-51. 10.3109/0954898X.2012.740140. Retrieved from https://hdl.handle.net/10161/17871.
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Assistant Professor of Neurobiology
My laboratory studies how the retina processes visual scenes and transmits this information to the brain. We use multi-electrode arrays to record the activity of hundreds of retina neurons simultaneously in conjunction with transgenic mouse lines and chemogenetics to manipulate neural circuit function. We are interested in three major areas. First, we work to understand how neurons in the retina are functionally connected. Second we are studying how light-adaptation and circadian rhythms a