Bayesian Learning of Loglinear Models for Neural Connectivity

TL;DR

This paper introduces a Bayesian framework for learning neural connectivity structures, extending traditional models to include higher-order interactions and applying MCMC methods to infer changes in neural firing patterns under different stimuli.

Contribution

It generalizes the Boltzmann machine model to higher-order interactions and develops a Bayesian inference approach using MCMC and Laplace's method for neural connectivity analysis.

Findings

Successfully applied to synthetic data for validation.

Detected differences in neural interactions between attentional states.

Confirmed experimental hypotheses about state-dependent neural connectivity.

Abstract

This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is to provide statistical tools for detecting changes in firing patterns with changing stimuli. Our framework is not restricted to the well-understood case of pair interactions, but generalizes the Boltzmann machine model to allow for higher order interactions. The paper applies a Markov Chain Monte Carlo Model Composition (MC3) algorithm to search over connectivity structures and uses Laplace's method to approximate posterior probabilities of structures. Performance of the methods was tested on synthetic data. The models were also applied to data obtained by Vaadia on multi-unit recordings of several neurons in the visual cortex of a rhesus monkey in two different attentional states. Results confirmed the experimenters' conjecture that different attentional states were associated with different interaction structures.