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.
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