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

Department of Electronic and Electrical Engineering, Institute of Neuroscience, Trinity College Dublin; College Green, Ireland; Department of Statistics, Columbia University, 10027, New York, N.Y, USA

ABSTRACT A major open problem in systems neuroscience is to understand the re-lationship 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. c 2009 Optical Society of America OCIS codes: 330.4060, 330.4150, 330.7310, 330.5310.

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    ABSTRACT: The estimation of visual motion has long been studied as a paradigmatic neural computation, and multiple models have been advanced to explain behavioral and neural responses to motion signals. A broad class of models, originating with the Reichardt correlator model, proposes that animals estimate motion by computing a temporal cross-correlation of light intensities from two neighboring points in visual space. These models provide a good description of experimental data in specific contexts but cannot explain motion percepts in stimuli lacking pairwise correlations. Here, we develop a theoretical formalism that can accommodate diverse stimuli and behavioral goals. To achieve this, we treat motion estimation as a problem of Bayesian inference. Pairwise models emerge as one component of the generalized strategy for motion estimation. However, correlation functions beyond second order enable more accurate motion estimation. Prior expectations that are asymmetric with respect to bright and dark contrast use correlations of both even and odd orders, and we show that psychophysical experiments using visual stimuli with symmetric probability distributions for contrast cannot reveal whether the subject uses odd-order correlators for motion estimation. This result highlights a gap in previous experiments, which have largely relied on symmetric contrast distributions. Our theoretical treatment provides a natural interpretation of many visual motion percepts, indicates that motion estimation should be revisited using a broader class of stimuli, demonstrates how correlation-based motion estimation is related to stimulus statistics, and provides multiple experimentally testable predictions.
    Proceedings of the National Academy of Sciences 08/2011; 108(31):12909-14. · 9.74 Impact Factor


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