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  • title: Learning rule influences recurrent network representations but not attractor structure in decision-making tasks
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            Learning rule influences recurrent network representations but not attractor structure in decision-making tasks
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            Learning rule influences recurrent network representations but not attractor structure in decision-making tasks

            Dec 6, 2021

            Speakers

            BJM

            Brandon J. McMahan

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            Michael Kleinman

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            JCK

            Jonathan C. Kao

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            About

            Recurrent neural networks (RNNs) are popular tools for studying computational dynamics in neurobiological circuits. However, due to the dizzying array of design choices, it is unclear if computational dynamics unearthed from RNNs provide reliable neurobiological inferences. Addressing these questions is valuable in two ways. First, identification of invariant properties that persist in RNNs across a wide range of design choices are more likely to be candidate neurobiological mechanisms. Second,…

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            NeurIPS 2021

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