Estimating a Separably-Markov Random Field (SMuRF) from Binary Observations

TL;DR

This paper introduces a novel 2D separable random field model for neural spike data that captures trial-to-trial dynamics and learning latency, providing detailed insights into neural activity during learning processes.

Contribution

It develops new statistical and computational tools to estimate a separable 2D RF model of neural spiking, addressing limitations of classical state-space methods.

Findings

Successfully applied to pre-frontal cortex data in mice

Revealed detailed neural dynamics during fear learning

Provided interpretable characterization of neural activity

Abstract

A fundamental problem in neuroscience is to characterize the dynamics of spiking from the neurons in a circuit that is involved in learning about a stimulus or a contingency. A key limitation of current methods to analyze neural spiking data is the need to collapse neural activity over time or trials, which may cause the loss of information pertinent to understanding the function of a neuron or circuit. We introduce a new method that can determine not only the trial-to-trial dynamics that accompany the learning of a contingency by a neuron, but also the latency of this learning with respect to the onset of a conditioned stimulus. The backbone of the method is a separable two-dimensional (2D) random field (RF) model of neural spike rasters, in which the joint conditional intensity function of a neuron over time and trials depends on two latent Markovian state sequences that evolve separately but in parallel. Classical tools to estimate state-space models cannot be applied readily to our 2D separable RF model. We develop efficient statistical and computational tools to estimate the parameters of the separable 2D RF model. We apply these to data collected from neurons in the pre-frontal cortex (PFC) in an experiment designed to characterize the neural underpinnings of the associative learning of fear in mice. Overall, the separable 2D RF model provides a detailed, interpretable, characterization of the dynamics of neural spiking that accompany the learning of a contingency.