| 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 | 2213 | 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 | 760 | 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 | 619 | 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 | 294 | 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 | 248 | 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 | 196 | 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 | 176 | 2002 |
| Correlation and Synchrony Transfer in Integrate-and-Fire Neurons:<? format?> Basic Properties and Consequences for Coding E Shea-Brown, K Josić, J De La Rocha, B Doiron Physical review letters 100 (10), 108102, 2008 | 165 | 2008 |
| Stochastic differential equation models for ion channel noise in Hodgkin-Huxley neurons JH Goldwyn, NS Imennov, M Famulare, E Shea-Brown Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 83 (4 …, 2011 | 163 | 2011 |
| 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 | 163 | 2007 |
| 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 | 146 | 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 | 144 | 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 | 140 | 2006 |
| Globally coupled oscillator networks E Brown, P Holmes, J Moehlis Perspectives and Problems in Nolinear Science: A Celebratory Volume in Honor …, 2003 | 138 | 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 | 133 | 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 | 110 | 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 | 92 | 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 | 91 | 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 (1), 183-214, 2018 | 82 | 2018 |
| Dimensionality compression and expansion in deep neural networks S Recanatesi, M Farrell, M Advani, T Moore, G Lajoie, E Shea-Brown arXiv preprint arXiv:1906.00443, 2019 | 78 | 2019 |
