A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements

Daniel Durstewitz

doi:10.1371/journal.pcbi.1005542

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Open Access

Peer-reviewed

Research Article

A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements

Daniel Durstewitz

* E-mail: daniel.durstewitz@zi-mannheim.de

Affiliation Dept. of Theoretical Neuroscience, Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, Medical Faculty Mannheim/ Heidelberg University, Mannheim, Germany

http://orcid.org/0000-0002-9340-3786

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A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements

Daniel Durstewitz

Published: June 2, 2017
https://doi.org/10.1371/journal.pcbi.1005542

About the Authors

Daniel Durstewitz

* E-mail: daniel.durstewitz@zi-mannheim.de

Affiliation Dept. of Theoretical Neuroscience, Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, Medical Faculty Mannheim/ Heidelberg University, Mannheim, Germany

http://orcid.org/0000-0002-9340-3786

Competing Interests

The author has declared that no competing interests exist.

Author Contributions

Conceptualization: DD. Formal analysis: DD. Funding acquisition: DD. Methodology: DD. Software: DD. Writing – original draft: DD. Writing – review & editing: DD.

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Working memory
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Dynamical systems
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Algorithms
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Approximation methods
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Eigenvalues
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Nonlinear dynamics
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Decision making
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Recurrent neural networks
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