Eric Shea-Brown, Ph.D.

Affiliations: 
Applied Mathematics University of Washington, Seattle, Seattle, WA 
Area:
computational neuroscience
Website:
http://faculty.washington.edu/etsb/eric.html
Google:
"Eric Shea-Brown"
Mean distance: 17.08 (cluster 17)
 
Cross-listing: MathTree

Parents

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Philip Holmes grad student 2004 Princeton
 (Neural oscillators and integrators in the dynamics of decision tasks)
John Rinzel grad student 2004-2007 NYU
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Publications

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Weber AI, Shea-Brown E, Rieke F. (2021) Identification of multiple noise sources improves estimation of neural responses across stimulus conditions. Eneuro
Recanatesi S, Farrell M, Lajoie G, et al. (2021) Predictive learning as a network mechanism for extracting low-dimensional latent space representations. Nature Communications. 12: 1417
Gutierrez GJ, Rieke F, Shea-Brown ET. (2021) Nonlinear convergence boosts information coding in circuits with parallel outputs. Proceedings of the National Academy of Sciences of the United States of America. 118
Stern M, Shea-Brown E. (2020) Network Dynamics Governed by Lyapunov Functions: From Memory to Classification. Trends in Neurosciences
de Vries SEJ, Lecoq JA, Buice MA, et al. (2019) A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex. Nature Neuroscience
Recanatesi S, Ocker GK, Buice MA, et al. (2019) Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity. Plos Computational Biology. 15: e1006446
Knox JE, Harris KD, Graddis N, et al. (2019) High-resolution data-driven model of the mouse connectome. Network Neuroscience (Cambridge, Mass.). 3: 217-236
Cayco-Gajic NA, Zylberberg J, Shea-Brown E. (2018) A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural Data. Entropy (Basel, Switzerland). 20
Kass RE, Amari SI, Arai K, et al. (2018) Computational Neuroscience: Mathematical and Statistical Perspectives. Annual Review of Statistics and Its Application. 5: 183-214
Brinkman BAW, Rieke F, Shea-Brown E, et al. (2018) Predicting how and when hidden neurons skew measured synaptic interactions. Plos Computational Biology. 14: e1006490
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