Eric Shea-Brown, Ph.D.

Applied Mathematics University of Washington, Seattle, Seattle, WA 
computational neuroscience
"Eric Shea-Brown"
Mean distance: 17.08 (cluster 17)
Cross-listing: MathTree


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Philip J. 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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Liu YH, Baratin A, Cornford J, et al. (2023) How connectivity structure shapes rich and lazy learning in neural circuits. Arxiv
Zdeblick DN, Shea-Brown ET, Witten DM, et al. (2023) Modeling functional cell types in spike train data. Biorxiv : the Preprint Server For Biology
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
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