Modeling Neural Activity with Conditionally Linear Dynamical Systems

TL;DR

This paper introduces Conditionally Linear Dynamical System (CLDS) models that combine Gaussian Process priors with linear dynamics to effectively characterize complex neural activity, especially in data-limited scenarios.

Contribution

The paper presents a novel CLDS framework that captures nonlinear neural dynamics conditioned on task variables, enabling transparent interpretation and efficient Bayesian inference.

Findings

CLDS models perform well with limited data.

Effective modeling of thalamic neuron activity.

Application to motor cortex during reaching tasks.

Abstract

Neural population activity exhibits complex, nonlinear dynamics, varying in time, over trials, and across experimental conditions. Here, we develop Conditionally Linear Dynamical System (CLDS) models as a general-purpose method to characterize these dynamics. These models use Gaussian Process (GP) priors to capture the nonlinear dependence of circuit dynamics on task and behavioral variables. Conditioned on these covariates, the data is modeled with linear dynamics. This allows for transparent interpretation and tractable Bayesian inference. We find that CLDS models can perform well even in severely data-limited regimes (e.g. one trial per condition) due to their Bayesian formulation and ability to share statistical power across nearby task conditions. In example applications, we apply CLDS to model thalamic neurons that nonlinearly encode heading direction and to model motor cortical neurons during a cued reaching task.