Eric Shea-Brown
Eric Shea-Brown
Applied Mathematics, University of Washington
Verified email at - Homepage
Cited by
Cited by
The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks.
R Bogacz, E Brown, J Moehlis, P Holmes, JD Cohen
Psychological review 113 (4), 700, 2006
Correlation between neural spike trains increases with firing rate
J De La Rocha, B Doiron, E Shea-Brown, K Josić, A Reyes
Nature 448 (7155), 802-806, 2007
On the phase reduction and response dynamics of neural oscillator populations
E Brown, J Moehlis, P Holmes
Neural computation 16 (4), 673-715, 2004
A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex
SEJ de Vries, JA Lecoq, MA Buice, PA Groblewski, GK Ocker, M Oliver, ...
Nature neuroscience 23 (1), 138-151, 2020
The what and where of adding channel noise to the Hodgkin-Huxley equations
JH Goldwyn, E Shea-Brown
PLoS computational biology 7 (11), e1002247, 2011
Impact of network structure and cellular response on spike time correlations
J Trousdale, Y Hu, E Shea-Brown, K Josić
PLoS computational biology 8 (3), e1002408, 2012
Mechanisms underlying dependencies of performance on stimulus history in a two-alternative forced-choice task
RY Cho, LE Nystrom, ET Brown, AD Jones, TS Braver, PJ Holmes, ...
Cognitive, Affective, & Behavioral Neuroscience 2 (4), 283-299, 2002
Correlation and synchrony transfer in integrate-and-fire neurons: basic properties and consequences for coding
E Shea-Brown, K Josić, J De La Rocha, B Doiron
Physical review letters 100 (10), 108102, 2008
Toward closed-loop optimization of deep brain stimulation for Parkinson's disease: concepts and lessons from a computational model
X Feng, B Greenwald, H Rabitz, E Shea-Brown, R Kosut
Journal of neural engineering 4 (2), L14, 2007
Stochastic differential equation models for ion channel noise in Hodgkin-Huxley neurons
JH Goldwyn, NS Imennov, M Famulare, E Shea-Brown
Physical Review E 83 (4), 041908, 2011
Optimal deep brain stimulation of the subthalamic nucleus—a computational study
XJ Feng, E Shea-Brown, B Greenwald, R Kosut, H Rabitz
Journal of computational neuroscience 23, 265-282, 2007
Direction-selective circuits shape noise to ensure a precise population code
J Zylberberg, J Cafaro, MH Turner, E Shea-Brown, F Rieke
Neuron 89 (2), 369-383, 2016
Optimal inputs for phase models of spiking neurons
J Moehlis, E Shea-Brown, H Rabitz
Journal of computational and nonlinear dynamics 1 (4), 358-367, 2006
Globally coupled oscillator networks
E Brown, P Holmes, J Moehlis
Perspectives and Problems in Nolinear Science: A Celebratory Volume in Honor …, 2003
Simple neural networks that optimize decisions
E Brown, J Gao, P Holmes, R Bogacz, M Gilzenrat, JD Cohen
International Journal of Bifurcation and Chaos 15 (03), 803-826, 2005
Stimulus-dependent correlations and population codes
K Josić, E Shea-Brown, B Doiron, J de la Rocha
Neural computation 21 (10), 2774-2804, 2009
Motif statistics and spike correlations in neuronal networks
Y Hu, J Trousdale, K Josić, E Shea-Brown
Journal of Statistical Mechanics: Theory and Experiment 2013 (03), P03012, 2013
High-resolution data-driven model of the mouse connectome
JE Knox, KD Harris, N Graddis, JD Whitesell, H Zeng, JA Harris, ...
Network Neuroscience 3 (1), 217-236, 2018
Computational neuroscience: Mathematical and statistical perspectives
RE Kass, SI Amari, K Arai, EN Brown, CO Diekman, M Diesmann, ...
Annual review of statistics and its application 5, 183-214, 2018
The influence of spike rate and stimulus duration on noradrenergic neurons
E Brown, J Moehlis, P Holmes, E Clayton, J Rajkowski, G Aston-Jones
Journal of computational neuroscience 17, 13-29, 2004
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