Author

Yuhan Helena Liu

Allen Institute for Brain ScienceORCID 0000-0001-7269-8338Unclaimed ORCID profile · follow to get notified of new preprints

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Approach:experimental assay· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Eric Shea‐BrowniD· 2 shared Ştefan MihalaşiD· 2 shared Alberto AntoniettiiD· 1 shared Balázs MészárosiD· 1 shared Brendan A. BicknelliD· 1 shared Christof Koch AiD· 1 shared Dan F. M. GoodmaniD· 1 shared Dana MastrovitoiD· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersNeuroscience

Eric Shea‐BrownORCID

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3 papers

Recent·Most cited

Spiking neural network models of sound localisation via a massively collaborative process

Marcus Ghosh, Karim G. Habashy, Francesco De Santis, Tomas Fiers, Dilay Fidan Erçelik, Balázs Mészáros, Zachary Friedenberger, Gabriel Béna, Mingxuan Hong, Umar Abubacar, Rory T. Byrne, Juan Luis Riquelme, Yuhan Helena Liu, Ido Aizenbud, Brendan A. Bicknell, , Dan F. M. Goodman

The paper describes a massively collaborative project using spiking neural networks to study sound localization, involving 31 researchers across multiple countries.

eNeuro · Jun 26, 2025Peer-reviewedNeuroscience

Transition to chaos separates learning regimes and relates to measure of consciousness in recurrent neural networks

Dana Mastrovito, Yuhan Helena Liu, Łukasz Kuśmierz, Eric Shea‐Brown, Christof Koch A, Ştefan Mihalaş

The paper shows that chaotic dynamics in recurrent neural networks separate learning regimes and correlate with a consciousness measure, suggesting a link between critical dynamics, learning, and consciousness.

bioRxiv · May 15, 2024 · PreprintNeuroscience

A solution to temporal credit assignment using cell-type-specific modulatory signals

Yuhan Helena Liu, Stephen J Smith, Ştefan Mihalaş, Eric Shea‐Brown, Uygar Sümbül

This paper proposes a learning rule for recurrent spiking neural networks that incorporates cell-type-specific modulatory signals to improve temporal credit assignment.

Proceedings of the National Academy of Sciences · Dec 16, 2021Peer-reviewedNeuroscience