Author

Ben Tsuda

Salk Institute for Biological StudiesORCID 0000-0003-2484-9044Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 2

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Approach:deep learning· 1

Frequent Co-Authors

Hava T. SiegelmanniD· 2 shared Kay M. TyeiD· 2 shared Terrence J. Sejnowski A BiD· 2 shared Stefan C. PateiD· 1 shared

Activity by Year

2020-2021 · peak 1/yr

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4 top collaborators · 6 mutual co-author links

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2 shared papersNeuroscience

Hava T. SiegelmannORCID

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2 papers

Recent·Most cited

NeurosciencebioRxivPreprintMay 31, 2021

Neuromodulators generate multiple context-relevant behaviors in a recurrent neural network by shifting activity flows in hyperchannels

Ben Tsuda, Stefan C. Pate, Kay M. Tye…·Terrence J. Sejnowski A B

This paper shows how neuromodulators alter neural network behavior by shifting activity flows through hyperchannels, enhancing computational flexibility in a recurrent neural network model.

9 citations

statistical model

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NeuroscienceProceedings of the National Academy of SciencesPeer-reviewedNov 5, 2020

A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex

Ben Tsuda, Kay M. Tye, Hava T. Siegelmann, Terrence J. Sejnowski A B

This paper introduces a neural network framework called DynaMoE to model how the prefrontal cortex encodes, stores, and switches between schemas, demonstrating transfer learning and memory savings.

49 citations

deep learningstatistical model

𝕏 PostBluesky