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Abstract

A central question in sensory neuroscience is to understand how sensory information is represented in neural activity. A crucial step towards the solution of this problem is the characterization of the neuron’s receptive field (RF), which provides a quantitative description of those features of a rich sensory stimulus that modulate the firing rate of the neuron.
For visual neurons, the stimulus space can be very high dimensional, and RFs have to be estimated from neurophysiological recordings of limited size. The scarcity of data makes it paramount to have statistical methods which incorporate prior knowledge into the estimation process (Park Pillow 2011), as well as to provide quantitative estimates of uncertainty about the inferred RFs (Stevenson et al 2011). For many sensory areas, there are canonical parametric models of RF shapes – e.g., Gabor functions for RFs in primary visual cortex (V1) (Jones Palmer 1987). Bayesian methods provide a quantitative way of evaluating these models on empirical data by estimating the uncertainty of the inferred model parameters.
We present a technique for full Bayesian inference of the parameters of parametric RF models, focusing on Gabor-shapes for V1. We model the spike generation process by means of a generalized linear model (GLM, Paninski 2004), whose linear filter (i.e., RF) is parameterized as a time-modulated Gabor-function. Use of this model dramatically reduces the number of parameters required to describe the RF, and allows us to directly estimate the posterior distribution over interpretable model parameters (e.g. location, orientation, etc.). The resulting model is non-linear in the parameters. We present an efficient Markov Chain Monte Carlo procedure for inferring the full posterior distribution over model parameters.
We show that the method successfully detects the presence or absence of a RF in simulated data – even when the total number of spikes is very 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. Our current implementation is focused on the response of simple cells in V1, but the approach can readily be extended to other sensory areas or non-linear models of complex cells.