Authors

• Cole Hurwitz
• Akash Srivastava
• Kai Xu
• Justin Jude
• Matthew Perich
• Lee Miller
• Matthias Hennig

Published on

10/28/2021

Latent dynamics models have emerged as powerful tools for modeling and interpreting neural population activity. Recently, there has been a focus on incorporating simultaneously measured behaviour into these models to further disentangle sources of neural variability in their latent space. These approaches, however, are limited in their ability to capture the underlying neural dynamics (e.g. linear) and in their ability to relate the learned dynamics back to the observed behaviour (e.g. no time lag). To this end, we introduce Targeted Neural Dynamical Modeling (TNDM), a nonlinear state-space model that jointly models the neural activity and external behavioural variables. TNDM decomposes neural dynamics into behaviourally relevant and behaviourally irrelevant dynamics; the relevant dynamics are used to reconstruct the behaviour through a flexible linear decoder and both sets of dynamics are used to reconstruct the neural activity through a linear decoder with no time lag. We implement TNDM as a sequential variational autoencoder and validate it on simulated recordings and recordings taken from the premotor and motor cortex of a monkey performing a center-out reaching task. We show that TNDM is able to learn low-dimensional latent dynamics that are highly predictive of behaviour without sacrificing its fit to the neural data.

This paper has been published at NeurIPS 2021

Please cite our work using the BibTeX below.

@misc{hurwitz2021targeted, title={Targeted Neural Dynamical Modeling}, author={Cole Hurwitz and Akash Srivastava and Kai Xu and Justin Jude and Matthew G. Perich and Lee E. Miller and Matthias H. Hennig}, year={2021}, eprint={2110.14853}, archivePrefix={arXiv}, primaryClass={q-bio.NC} }