One-hot Generalized Linear Model for Switching Brain State Discovery

TL;DR

This paper introduces a novel prior-informed state-switching GLM that models dynamic neural interactions, capturing underlying anatomical constraints and improving interpretability and predictive performance in neural data analysis.

Contribution

It proposes a new prior-informed GLM with learnable priors that better reflect biological plausibility and reveal underlying neural connectomes.

Findings

Effectively recovers true interaction structures in simulations.

Achieves highest predictive likelihood on real neural data.

Enhances interpretability of neural interaction structures.

Abstract

Exposing meaningful and interpretable neural interactions is critical to understanding neural circuits. Inferred neural interactions from neural signals primarily reflect functional interactions. In a long experiment, subject animals may experience different stages defined by the experiment, stimuli, or behavioral states, and hence functional interactions can change over time. To model dynamically changing functional interactions, prior work employs state-switching generalized linear models with hidden Markov models (i.e., HMM-GLMs). However, we argue they lack biological plausibility, as functional interactions are shaped and confined by the underlying anatomical connectome. Here, we propose a novel prior-informed state-switching GLM. We introduce both a Gaussian prior and a one-hot prior over the GLM in each state. The priors are learnable. We will show that the learned prior should capture the state-constant interaction, shedding light on the underlying anatomical connectome and revealing more likely physical neuron interactions. The state-dependent interaction modeled by each GLM offers traceability to capture functional variations across multiple brain states. Our methods effectively recover true interaction structures in simulated data, achieve the highest predictive likelihood with real neural datasets, and render interaction structures and hidden states more interpretable when applied to real neural data.