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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A general recurrent state space framework for modeling neural dynamics during decision-making

Jan 1, 2020·
David M Zoltowski
,
Jonathan W Pillow
,
Scott W Linderman
· 0 min read
PDF Cite Code
Type
Conference paper
Publication
Proceedings of the International Conference on Machine Learning (ICML)
Last updated on Nov 19, 2024
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