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

ICML, pp. 11680-11691, 2020.

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We introduced a unifying framework for decision-making models based on constrained recurrent switching linear dynamical systems

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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. Here we propose a general framework for mo...更多

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