Optimal decoding of stimulus velocity using a probabilistic model of ganglion cell populations in primate retina.

Ed Lalor, Yashar Ahmadian, and Liam Paninski

JOSA A (special issue on ideal observers and efficiency) 26: B25-42

A major open problem in systems neuroscience is to understand the relationship between behavior and the detailed spiking properties of neural populations. In this work, we assess how faithfully velocity information can be decoded from a population of spiking model retinal neurons whose spatiotemporal receptive fields and ensemble spike-train dynamics are closely matched to real data. We describe how to compute the optimal Bayesian estimate of image velocity given the population spike train response, and show that, given complete information about the displayed image, the spike train ensemble signals speed with an average relative precision of about 2% across a specific set of stimulus conditions. We further show how to compute the Bayesian velocity estimate in the case where we only have some a priori information about the (naturalistic) correlation structure of the image, but do not know the image explicitly. As expected, the performance of the Bayesian decoder is shown to be less accurate with decreasing prior image information. There turns out to be a close mathematical connection between a biologically-plausible "motion energy" method for decoding the velocity and the optimal Bayesian decoder in the case that the image is not known. Simulations using the motion energy method reveal that it results in an average relative precision of only 10% across the same set of stimulus conditions. Estimation performance is rather insensitive to the details of the precise receptive field location, correlated activity between cells, and spike timing.
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