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Abstract

crucial step towards understanding how the external world is represented by sensory neurons is the characterization of neural receptive fields. Advances in experimental methods give increasing opportunity to study sensory processing in behaving animals, but also necessitate the ability to estimate receptive fields from very small spike-counts. For visual neurons, the stimulus space can be very high dimensional, raising challenges for data-analysis: How can one accurately estimate neural receptive fields using only a few spikes, and obtain quantitative uncertainty-estimates about tuning properties (such as location and preferred orientation)? For many sensory areas, there are canonical parametric models of receptive field shapes (e.g., Gabor functions for primary visual cortex) which can be used to constrain receptive fields – we will use such parametric models for receptive field estimation in low-data regimes using full Bayesian inference. We will focus on modelling simple cells in primary visual cortex, but our approach will be applicable more generally. We model the spike generation process using a generalized linear model (GLM), with a receptive field parameterized as a time-modulated Gabor. Use of the parametric model dramatically reduces the number of parameters, and allows us to directly estimate the posterior distribution over interpretable model parameters. We develop an efficient Markov Chain Monte Carlo procedure which is adapted to receptive field estimation from movie-data, by exploiting spatio-temporal separability of receptive fields. We show that the method successfully detects the presence or absence of a receptive field in simulated data even when the total number of spikes is low, and can correctly recover ground-truth parameters. When applied to electrophysiological recordings, it returns estimates of model parameters which are consistent across different subsets of the data. In comparison with non-parametric methods based on Gaussian Processes, we find that it leads to better spike-prediction performance.