Eric Shea-Brown, Ph.D.
Affiliations: | Applied Mathematics | University of Washington, Seattle, Seattle, WA |
Area:
computational neuroscienceWebsite:
http://faculty.washington.edu/etsb/eric.htmlGoogle:
"Eric Shea-Brown"Mean distance: 17.08 (cluster 17)
Cross-listing: MathTree
Parents
Sign in to add mentor| Philip Holmes | grad student | 2004 | Princeton | |
| (Neural oscillators and integrators in the dynamics of decision tasks) | ||||
| John Rinzel | grad student | 2004-2007 | NYU | |
Children
Sign in to add trainee| Joshua H. Goldwyn | grad student | 2011 | University of Washington |
| Nicholas Cain | grad student | 2012 | University of Washington |
| Guillaume Lajoie | grad student | 2013 | University of Washington |
| Kameron Decker Harris | grad student | 2012-2017 | University of Washington |
| Andrea K. Barreiro | post-doc | University of Washington (MathTree) | |
| Hannah Choi | post-doc | ||
| Gabrielle J. Gutierrez | post-doc | 2016- | University of Washington |
| Joel Zylberberg | post-doc | 2012-2015 | University of Washington |
Collaborators
Sign in to add collaborator| Fred Rieke | collaborator | University of Washington | |
| Jaime de la Rocha | collaborator | 2004-2008 | Center for Neural Science, NYU |
BETA: Related publications
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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 |