Decoding of stimulus velocity using a model of ganglion cell populations in primate retina.

Abstract

Event Abstract Back to Event Decoding of stimulus velocity using a model of ganglion cell populations in primate retina. The encoding of visual stimuli in the spike trains of retinal ganglion cells (RGCs) places limitations on subsequent visual processing. Here, we examine such limitations in the context of visual motion estimation. We describe the encoding of visual stimuli in spike trains using a recently developed statistical model for RGC populations (Pillow et al., Nature 454(7206): 995-999, 2008). The model includes spike-history effects and cross-coupling between cells of the same kind and of different kinds and, accurately captures the stimulus dependence and spatio-temporal correlation structure of population responses. It also provides a relatively tractable expression for the probability of observing a given spike train, conditioned on the stimulus. Based on this model-based likelihood function, we construct an optimal (Bayesian) estimator for image velocity given the population spike train response. We implement two variants of this decoder. In the first, we assume the visual input is formed by translation of a known spatial intensity image. In the second, we assume only the (naturalistic) correlation structure of the intensity image is known, but do not know the image explicitly. Finally we explore a biologically-plausible ``motion energy'' method for decoding the velocity and show that, as with estimators based on spatio-temporal gradients, there is a close mathematical connection between this energy method and the optimal Bayesian decoder in the case that the image is not known. Through simulations, we show that the performance of the Bayesian decoder is less accurate with decreasing prior image information. Simulations across several different speeds and contrasts of a moving bar stimulus reveal that the Bayesian decoder with full image information achieves an average speed estimation precision of 2.7%, while the motion energy method results in an average speed estimation precision of only 7.8% across the same set of conditions. For both methods, estimation precision is shown to be better for more slowly moving stimuli and for stimuli with higher contrast. Human psychophysical performance on short duration velocity estimation tasks seems to be much better represented by the performance of the model in the case that the image is not known exactly a priori. Finally, we show that estimation performance is shown to be rather insensitive to the details of the precise receptive field location, correlated activity between cells, and spike timing. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Decoding of stimulus velocity using a model of ganglion cell populations in primate retina.. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.022 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 30 Jan 2009; Published Online: 30 Jan 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.