Abstract

The receptive field (RF) of a sensory neuron describes how the neuron integrates sensory stimuli over time and space. In typical experiments with naturalistic or flickering spatiotemporal stimuli, RFs are very high-dimensional, due to the large number of coefficients needed to specify an integration profile across time and space. Estimating these coefficients from small amounts of data poses a variety of challenging statistical and computational problems. Here we address these challenges by developing Bayesian reduced rank regression methods for RF estimation. This corresponds to modeling the RF as a sum of space-time separable (i.e., rank-1) filters. This approach substantially reduces the number of parameters needed to specify the RF, from 1K-10K down to mere 100s in the examples we consider, and confers substantial benefits in statistical power and computational efficiency. We introduce a novel prior over low-rank RFs using the restriction of a matrix normal prior to the manifold of low-rank matrices, and use “localized” row and column covariances to obtain sparse, smooth, localized estimates of the spatial and temporal RF components. We develop two methods for inference in the resulting hierarchical model: (1) a fully Bayesian method using blocked-Gibbs sampling; and (2) a fast, approximate method that employs alternating ascent of conditional marginal likelihoods. We develop these methods for Gaussian and Poisson noise models, and show that low-rank estimates substantially outperform full rank estimates using neural data from retina and V1.