Modeling Neural Activity with Conditionally Linear Dynamical Systems
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 62 references that are in the map. First posted on arXiv 25 Feb 2025.
Builds on
19 of 62 references.
References · 19
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7 more
- 2024Latent Diffusion for Neural Spiking Datacited by 12
- 2024Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systemscited by 11
- 2023Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trainscited by 5
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- 2021Hida-Mat'ern Kernelcited by 4
- 2024Comparing noisy neural population dynamics using optimal transport distancescited by 4
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Citing papers per year: 2026: 1.
1 paper.