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Jonathan Pillow

2 accepted papers

2020

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

ICML 2020poster

An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision. Fitting models of decision-making theory to neural activity helps answer this question, but current approaches limit the number of these models that we can fit to neural data. He…

Cited by 51Source ↗PDF ↗Scholar ↗
2020

Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations

ICML 2020poster

Gaussian Process Factor Analysis (GPFA) has been broadly applied to the problem of identifying smooth, low-dimensional temporal structure underlying large-scale neural recordings. However, spike trains are non-Gaussian, which motivates combining GPFA with discrete observation models for binned spike…

Cited by 23Source ↗PDF ↗Scholar ↗