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Abstract
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.
Notes
Notes
1. The cone width and minimal cone spacing (discussed further below) are two key parameters for the analysis here which are currently set by hand by the experimenter; incorrect parameter choices can be detected by visible mismatches between the spike-triggered averages and the inferred cone spacings. These parameters could in principle be selected by automatic criteria (e.g., cross-validation), but we have not yet pursued this approach.