Behavior-dLDS: A decomposed linear dynamical systems model for neural activity partially constrained by behavior

TL;DR

Behavior-dLDS is a novel model that disentangles neural subsystems related to behavior from internal computations, improving interpretability and scalability in large-scale neural recordings.

Contribution

It introduces behavior-decomposed linear dynamical systems (b-dLDS) for separating neural subsystems and relating them to behavior, with demonstrated advantages over existing models.

Findings

b-dLDS effectively decouples behavioral and internal neural dynamics on simulated data.

The model improves interpretability on nonlinear behavior-activation datasets.

b-dLDS scales to tens of thousands of neurons, revealing asymmetries in zebrafish neural connectivity.

Abstract

Brain-wide recordings of large-scale networks of neurons now provide an unprecedented view into how the brain drives behavior. However, brain activity contains both information directly related to behavior as well as the potential for many internal computations. Moreover, observable behavior is executed not only by the brain, but also by the spinal cord and peripheral nervous system. Behavior is a coarse-grained product of neural activity, and we thus take the view that it can be best represented by lower-dimensional latent neural dynamics. Capturing this indirect relationship while disambiguating behavior-generating networks from internal computations running in parallel requires new modeling approaches that can embody the parallel and distributed nature of large-scale neural populations. We thus present behavior-decomposed linear dynamical systems (b-dLDS) to disentangle simultaneously recorded subsystems and identify how the latent neural subsystems relate to behavior. We demonstrate the ability of b-dLDS to decouple behavioral vs. internal computations on controlled, simulated data, showing improvements over a state-of-the-art model that uses behavior to supervise all dynamics based on behavior. We also demonstrate b-dLDS's interpretability benefits on a task-driven RNN dataset featuring a nonlinear relationship between behavior and activations. We then show that b-dLDS can further scale up to tens of thousands of neurons by applying our model to a large-scale recording of a zebrafish hindbrain during the complex positional homeostasis behavior, wherein b-dLDS highlights asymmetry in behavior-related dynamic connectivity networks.