Modeling Neural Activity with Conditionally Linear Dynamical Systems
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917049664536576 |
|---|---|
| author | Geadah, Victor Nejatbakhsh, Amin Lipshutz, David Pillow, Jonathan W. Williams, Alex H. |
| author_facet | Geadah, Victor Nejatbakhsh, Amin Lipshutz, David Pillow, Jonathan W. Williams, Alex H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18347 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling Neural Activity with Conditionally Linear Dynamical Systems Geadah, Victor Nejatbakhsh, Amin Lipshutz, David Pillow, Jonathan W. Williams, Alex H. Neurons and Cognition Machine Learning 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. |
| title | Modeling Neural Activity with Conditionally Linear Dynamical Systems |
| topic | Neurons and Cognition Machine Learning |
| url | https://arxiv.org/abs/2502.18347 |