Bayesian latent structure discovery from multi-neuron recordings

TL;DR

This paper introduces a Bayesian hierarchical model that leverages temporal dependencies and noise modeling in multi-neuron spike train data to uncover latent neural circuit structures, improving upon traditional methods.

Contribution

It develops a novel hierarchical Bayesian extension of the GLM for neural data, integrating graph priors and Pólya-gamma augmentation for latent structure inference.

Findings

Successfully identified neural types and locations from spike trains.

Demonstrated effectiveness on synthetic and primate retina data.

Revealed latent patterns of neural organization.

Abstract

Neural circuits contain heterogeneous groups of neurons that differ in type, location, connectivity, and basic response properties. However, traditional methods for dimensionality reduction and clustering are ill-suited to recovering the structure underlying the organization of neural circuits. In particular, they do not take advantage of the rich temporal dependencies in multi-neuron recordings and fail to account for the noise in neural spike trains. Here we describe new tools for inferring latent structure from simultaneously recorded spike train data using a hierarchical extension of a multi-neuron point process model commonly known as the generalized linear model (GLM). Our approach combines the GLM with flexible graph-theoretic priors governing the relationship between latent features and neural connectivity patterns. Fully Bayesian inference via P\'olya-gamma augmentation of the resulting model allows us to classify neurons and infer latent dimensions of circuit organization from correlated spike trains. We demonstrate the effectiveness of our method with applications to synthetic data and multi-neuron recordings in primate retina, revealing latent patterns of neural types and locations from spike trains alone.