Inferring Nonlinear Neuronal Computation Based on Physiologically Plausible Inputs

James M. McFarland; Yuwei Cui; Daniel A. Butts

doi:10.1371/journal.pcbi.1003143

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Open Access

Peer-reviewed

Research Article

Inferring Nonlinear Neuronal Computation Based on Physiologically Plausible Inputs

James M. McFarland ,

* E-mail: jmmcfarl@umd.edu

Affiliation Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland, United States of America
⨯
Yuwei Cui,

Affiliation Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland, United States of America
⨯
Daniel A. Butts

Affiliation Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland, United States of America
⨯

Inferring Nonlinear Neuronal Computation Based on Physiologically Plausible Inputs

James M. McFarland,
Yuwei Cui,
Daniel A. Butts

Published: July 18, 2013
https://doi.org/10.1371/journal.pcbi.1003143

About the Authors

James M. McFarland

* E-mail: jmmcfarl@umd.edu

Affiliation Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland, United States of America

Yuwei Cui

Affiliation Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland, United States of America

Daniel A. Butts

Affiliation Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland, United States of America

Competing Interests

The authors have declared that no competing interests exist.

Author Contributions

Analyzed the data: JMM YC DAB. Wrote the paper: JMM DAB. Developed the model: JMM YC DAB.

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Neurons
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Retinal ganglion cells
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Sensory perception
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Linear filters
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Optimization
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Vision
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Sensory physiology
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