Scalable Bayesian inference for high-dimensional neural receptive fields

Abstract

We examine the problem of rapidly and efficiently estimating a neuron’s linear receptive field (RF) from responses to high-dimensional stimuli. This problem poses important statistical and computational challenges. Statistical challenges arise from the need for strong regularization when using correlated stimuli in high-dimensional parameter spaces, while computational challenges arise from extensive time and memory costs associated with evidence-optimization and inference in high-dimensional settings. Here we focus on novel methods for scaling up automatic smoothness determination (ASD), an empirical Bayesian method for RF estimation, to high-dimensional settings. First, we show that using a zero-padded Fourier domain representation and a “coarse-to-fine” evidence optimization strategy gives substantial improvements in speed and memory, while maintaining exact numerical accuracy. We then introduce a suite of scalable approximate methods that exploit Kronecker and Toeplitz structure in the stimulus autocovariance, which can be related to the method of expected log-likelihoods [1]. When applied together, these methods reduce the cost of estimating an RF with tensor order D and d coefficients per tensor dimension from O(d^3D) time and O(d^2D) space for standard ASD to O(Dd log d) time and O(Dd) space. We show that evidence optimization for a linear RF with 160K coefficients using 5K samples of data can be carried out on a laptop in < 2s.