David Zoltowski
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    • Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems
    • Modeling state-dependent communication between brain regions with switching nonlinear dynamical systems
    • Structured flexibility in recurrent neural networks via neuromodulation
    • Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics
    • Neural latents benchmark'21: evaluating latent variable models of neural population activity
    • Slice sampling reparameterization gradients
    • A general recurrent state space framework for modeling neural dynamics during decision-making
    • Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations
    • Modeling statistical dependencies in multi-region spike train data
    • Discrete stepping and nonlinear ramping dynamics underlie spiking responses of LIP neurons during decision-making
    • Scaling the Poisson GLM to massive neural datasets through polynomial approximations
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Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems

Jan 1, 2024·
Amber Hu
,
David Zoltowski
,
Aditya Nair
,
David Anderson
,
Lea Duncker
,
Scott Linderman
· 0 min read
Cite
Type
Journal article
Publication
arXiv preprint arXiv:2408.03330
Last updated on Oct 18, 2024
Modeling state-dependent communication between brain regions with switching nonlinear dynamical systems Jan 1, 2024 →

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