| Year |
Citation |
Score |
| 2023 |
Liu YH, Baratin A, Cornford J, Mihalas S, Shea-Brown E, Lajoie G. How connectivity structure shapes rich and lazy learning in neural circuits. Arxiv. PMID 37873007 |
0.527 |
|
| 2023 |
Zdeblick DN, Shea-Brown ET, Witten DM, Buice MA. Modeling functional cell types in spike train data. Biorxiv : the Preprint Server For Biology. PMID 36909648 DOI: 10.1101/2023.02.28.530327 |
0.301 |
|
| 2021 |
Weber AI, Shea-Brown E, Rieke F. Identification of multiple noise sources improves estimation of neural responses across stimulus conditions. Eneuro. PMID 34083382 DOI: 10.1523/ENEURO.0191-21.2021 |
0.73 |
|
| 2021 |
Recanatesi S, Farrell M, Lajoie G, Deneve S, Rigotti M, Shea-Brown E. Predictive learning as a network mechanism for extracting low-dimensional latent space representations. Nature Communications. 12: 1417. PMID 33658520 DOI: 10.1038/s41467-021-21696-1 |
0.732 |
|
| 2021 |
Gutierrez GJ, Rieke F, Shea-Brown ET. Nonlinear convergence boosts information coding in circuits with parallel outputs. Proceedings of the National Academy of Sciences of the United States of America. 118. PMID 33593894 DOI: 10.1073/pnas.1921882118 |
0.801 |
|
| 2020 |
Stern M, Shea-Brown E. Network Dynamics Governed by Lyapunov Functions: From Memory to Classification. Trends in Neurosciences. PMID 32386741 DOI: 10.1016/J.Tins.2020.04.002 |
0.316 |
|
| 2019 |
de Vries SEJ, Lecoq JA, Buice MA, Groblewski PA, Ocker GK, Oliver M, Feng D, Cain N, Ledochowitsch P, Millman D, Roll K, Garrett M, Keenan T, Kuan L, Mihalas S, ... ... Shea-Brown E, et al. A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex. Nature Neuroscience. PMID 31844315 DOI: 10.1038/S41593-019-0550-9 |
0.606 |
|
| 2019 |
Recanatesi S, Ocker GK, Buice MA, Shea-Brown E. Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity. Plos Computational Biology. 15: e1006446. PMID 31299044 DOI: 10.1371/Journal.Pcbi.1006446 |
0.39 |
|
| 2019 |
Knox JE, Harris KD, Graddis N, Whitesell JD, Zeng H, Harris JA, Shea-Brown E, Mihalas S. High-resolution data-driven model of the mouse connectome. Network Neuroscience (Cambridge, Mass.). 3: 217-236. PMID 30793081 DOI: 10.1162/netn_a_00066 |
0.723 |
|
| 2018 |
Cayco-Gajic NA, Zylberberg J, Shea-Brown E. A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural Data. Entropy (Basel, Switzerland). 20. PMID 33265579 DOI: 10.3390/e20070489 |
0.708 |
|
| 2018 |
Kass RE, Amari SI, Arai K, Brown EN, Diekman CO, Diesmann M, Doiron B, Eden UT, Fairhall AL, Fiddyment GM, Fukai T, Grün S, Harrison MT, Helias M, Nakahara H, ... ... Shea-Brown E, et al. Computational Neuroscience: Mathematical and Statistical Perspectives. Annual Review of Statistics and Its Application. 5: 183-214. PMID 30976604 DOI: 10.1146/annurev-statistics-041715-033733 |
0.599 |
|
| 2018 |
Brinkman BAW, Rieke F, Shea-Brown E, Buice MA. Predicting how and when hidden neurons skew measured synaptic interactions. Plos Computational Biology. 14: e1006490. PMID 30346943 DOI: 10.1371/Journal.Pcbi.1006490 |
0.73 |
|
| 2018 |
Choi H, Pasupathy A, Shea-Brown E. Predictive Coding in Area V4: Dynamic Shape Discrimination under Partial Occlusion. Neural Computation. 1-49. PMID 29566355 DOI: 10.1162/Neco_A_01072 |
0.537 |
|
| 2018 |
Cayco-Gajic N, Zylberberg J, Shea-Brown E. A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural Data Entropy. 20: 489. DOI: 10.3390/E20070489 |
0.678 |
|
| 2018 |
Hu Y, Brunton SL, Cain N, Mihalas S, Kutz JN, Shea-Brown E. Feedback through graph motifs relates structure and function in complex networks Physical Review E. 98. DOI: 10.1103/Physreve.98.062312 |
0.621 |
|
| 2017 |
Fyall AM, El-Shamayleh Y, Choi H, Shea-Brown E, Pasupathy A. Dynamic representation of partially occluded objects in primate prefrontal and visual cortex. Elife. 6. PMID 28925354 DOI: 10.7554/Elife.25784 |
0.538 |
|
| 2017 |
Ocker GK, Hu Y, Buice MA, Doiron B, Josić K, Rosenbaum R, Shea-Brown E. From the statistics of connectivity to the statistics of spike times in neuronal networks. Current Opinion in Neurobiology. 46: 109-119. PMID 28863386 DOI: 10.1016/J.Conb.2017.07.011 |
0.663 |
|
| 2017 |
Ocker GK, Josić K, Shea-Brown E, Buice MA. Linking structure and activity in nonlinear spiking networks. Plos Computational Biology. 13: e1005583. PMID 28644840 DOI: 10.1371/Journal.Pcbi.1005583 |
0.43 |
|
| 2017 |
Harris KD, Dashevskiy T, Mendoza J, Garcia AJ, Ramirez JM, Shea-Brown E. Different roles for inhibition in the rhythm-generating respiratory network. Journal of Neurophysiology. jn.00174.2017. PMID 28615332 DOI: 10.1152/Jn.00174.2017 |
0.747 |
|
| 2017 |
Zylberberg J, Pouget A, Latham PE, Shea-Brown E. Robust information propagation through noisy neural circuits. Plos Computational Biology. 13: e1005497. PMID 28419098 DOI: 10.1371/Journal.Pcbi.1005497 |
0.736 |
|
| 2017 |
Fyall AM, El-Shamayleh Y, Choi H, Shea-Brown E, Pasupathy A. Author response: Dynamic representation of partially occluded objects in primate prefrontal and visual cortex Elife. DOI: 10.7554/Elife.25784.031 |
0.464 |
|
| 2016 |
Lajoie G, Lin KK, Thivierge JP, Shea-Brown E. Encoding in Balanced Networks: Revisiting Spike Patterns and Chaos in Stimulus-Driven Systems. Plos Computational Biology. 12: e1005258. PMID 27973557 DOI: 10.1371/Journal.Pcbi.1005258 |
0.652 |
|
| 2016 |
Brinkman BA, Weber AI, Rieke F, Shea-Brown E. How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits? Plos Computational Biology. 12: e1005150. PMID 27741248 DOI: 10.1371/Journal.Pcbi.1005150 |
0.795 |
|
| 2016 |
Sharpee TO, Destexhe A, Kawato M, Sekulić V, Skinner FK, Wójcik DK, Chintaluri C, Cserpán D, Somogyvári Z, Kim JK, Kilpatrick ZP, Bennett MR, Josić K, Elices I, Arroyo D, ... ... Shea-Brown E, et al. 25th Annual Computational Neuroscience Meeting: CNS-2016 Bmc Neuroscience. 17: 54. PMID 27534393 DOI: 10.1186/S12868-016-0283-6 |
0.733 |
|
| 2016 |
Zylberberg J, Cafaro J, Turner MH, Shea-Brown E, Rieke F. Direction-Selective Circuits Shape Noise to Ensure a Precise Population Code. Neuron. 89: 369-83. PMID 26796691 DOI: 10.1016/J.Neuron.2015.11.019 |
0.816 |
|
| 2015 |
Zylberberg J, Shea-Brown E. Input nonlinearities can shape beyond-pairwise correlations and improve information transmission by neural populations. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 92: 062707. PMID 26764727 DOI: 10.1103/Physreve.92.062707 |
0.738 |
|
| 2015 |
Leen DA, Shea-Brown E. A Simple Mechanism for Beyond-Pairwise Correlations in Integrate-and-Fire Neurons. Journal of Mathematical Neuroscience. 5: 30. PMID 26265217 DOI: 10.1186/S13408-015-0030-9 |
0.492 |
|
| 2015 |
Schwemmer MA, Fairhall AL, Denéve S, Shea-Brown ET. Constructing Precisely Computing Networks with Biophysical Spiking Neurons. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 35: 10112-34. PMID 26180189 DOI: 10.1523/Jneurosci.4951-14.2015 |
0.673 |
|
| 2015 |
Cayco-Gajic NA, Zylberberg J, Shea-Brown E. Triplet correlations among similarly tuned cells impact population coding. Frontiers in Computational Neuroscience. 9: 57. PMID 26042024 DOI: 10.3389/Fncom.2015.00057 |
0.738 |
|
| 2015 |
Cayco-Gajic NA, Zylberberg J, Shea-Brown E. Triplet correlations among similarly tuned cells impact population coding Frontiers in Computational Neuroscience. 9. DOI: 10.3389/fncom.2015.00057 |
0.66 |
|
| 2015 |
Cayco-Gajic A, Zylberberg J, Shea-Brown E. Curvature of dendritic nonlinearities modulates higher-order spiking correlations Bmc Neuroscience. 16. DOI: 10.1186/1471-2202-16-S1-P227 |
0.732 |
|
| 2015 |
Zylberberg J, Cafaro J, Turner M, Rieke F, Shea-Brown E. Limited range correlations, when modulated by firing rate, can substantially improve neural population coding Bmc Neuroscience. 16. DOI: 10.1186/1471-2202-16-S1-O16 |
0.818 |
|
| 2014 |
Lajoie G, Thivierge JP, Shea-Brown E. Structured chaos shapes spike-response noise entropy in balanced neural networks. Frontiers in Computational Neuroscience. 8: 123. PMID 25324772 DOI: 10.3389/Fncom.2014.00123 |
0.665 |
|
| 2014 |
Hu Y, Trousdale J, Josić K, Shea-Brown E. Local paths to global coherence: cutting networks down to size. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 89: 032802. PMID 24730894 DOI: 10.1103/Physreve.89.032802 |
0.331 |
|
| 2014 |
Hu Y, Zylberberg J, Shea-Brown E. The sign rule and beyond: boundary effects, flexibility, and noise correlations in neural population codes. Plos Computational Biology. 10: e1003469. PMID 24586128 DOI: 10.1371/Journal.Pcbi.1003469 |
0.747 |
|
| 2014 |
Barreiro AK, Gjorgjieva J, Rieke F, Shea-Brown E. When do microcircuits produce beyond-pairwise correlations? Frontiers in Computational Neuroscience. 8: 10. PMID 24567715 DOI: 10.3389/Fncom.2014.00010 |
0.81 |
|
| 2014 |
Zylberberg J, Shea-Brown E. When does recurrent connectivity improve neural population coding? Bmc Neuroscience. 15. DOI: 10.1186/1471-2202-15-S1-P49 |
0.756 |
|
| 2014 |
Lajoie G, Thivierge J, Shea-Brown E. Structured chaos shapes joint spike-response noise entropy in temporally driven balanced networks Bmc Neuroscience. 15. DOI: 10.1186/1471-2202-15-S1-P48 |
0.686 |
|
| 2014 |
Brinkman BAW, Weber A, Rieke F, Shea-Brown E. Noise- and stimulus-dependence of the optimal encoding nonlinearities in a simple ON/OFF retinal circuit model Bmc Neuroscience. 15. DOI: 10.1186/1471-2202-15-S1-P47 |
0.741 |
|
| 2013 |
Trousdale J, Hu Y, Shea-Brown E, Josi? K. A generative spike train model with time-structured higher order correlations. Frontiers in Computational Neuroscience. 7: 84. PMID 23908626 DOI: 10.3389/Fncom.2013.00084 |
0.443 |
|
| 2013 |
Lajoie G, Lin KK, Shea-Brown E. Chaos and reliability in balanced spiking networks with temporal drive. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 87: 052901. PMID 23767592 DOI: 10.1103/Physreve.87.052901 |
0.623 |
|
| 2013 |
Cayco-Gajic NA, Shea-Brown E. Neutral stability, rate propagation, and critical branching in feedforward networks. Neural Computation. 25: 1768-806. PMID 23607560 DOI: 10.1162/Neco_A_00461 |
0.404 |
|
| 2013 |
Cain N, Barreiro AK, Shadlen M, Shea-Brown E. Neural integrators for decision making: a favorable tradeoff between robustness and sensitivity. Journal of Neurophysiology. 109: 2542-59. PMID 23446688 DOI: 10.1152/Jn.00976.2012 |
0.785 |
|
| 2013 |
Cain N, Shea-Brown E. Impact of correlated neural activity on decision-making performance. Neural Computation. 25: 289-327. PMID 23148409 DOI: 10.1162/Neco_A_00398 |
0.674 |
|
| 2013 |
Zylberberg J, Turner M, Hu Y, Cafaro J, Schwartz G, Rieke F, Shea-Brown E. Consistency requirements determine optimal noise correlations in neural populations Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-F1 |
0.814 |
|
| 2013 |
Hu Y, Trousdale J, Josić K, Shea-Brown E. Motif statistics and spike correlations in neuronal networks Journal of Statistical Mechanics: Theory and Experiment. 2013. DOI: 10.1088/1742-5468/2013/03/P03012 |
0.387 |
|
| 2012 |
Fairhall A, Shea-Brown E, Barreiro A. Information theoretic approaches to understanding circuit function. Current Opinion in Neurobiology. 22: 653-9. PMID 22795220 DOI: 10.1016/J.Conb.2012.06.005 |
0.761 |
|
| 2012 |
Goldwyn JH, Rubinstein JT, Shea-Brown E. A point process framework for modeling electrical stimulation of the auditory nerve. Journal of Neurophysiology. 108: 1430-52. PMID 22673331 DOI: 10.1152/Jn.00095.2012 |
0.789 |
|
| 2012 |
Barreiro AK, Thilo EL, Shea-Brown E. A-current and type I/type II transition determine collective spiking from common input. Journal of Neurophysiology. 108: 1631-45. PMID 22673330 DOI: 10.1152/Jn.00928.2011 |
0.769 |
|
| 2012 |
Cain N, Shea-Brown E. Computational models of decision making: integration, stability, and noise. Current Opinion in Neurobiology. 22: 1047-53. PMID 22591667 DOI: 10.1016/J.Conb.2012.04.013 |
0.654 |
|
| 2012 |
Trousdale J, Hu Y, Shea-Brown E, Josić K. Impact of network structure and cellular response on spike time correlations. Plos Computational Biology. 8: e1002408. PMID 22457608 DOI: 10.1371/Journal.Pcbi.1002408 |
0.426 |
|
| 2012 |
Leen DA, Shea-Brown E. A simple mechanism for higher-order correlations in integrate-and-fire neurons Bmc Neuroscience. 13. DOI: 10.1186/1471-2202-13-S1-P45 |
0.465 |
|
| 2012 |
Cain N, Shea-Brown E. Speed and accuracy in decision making: input correlations and performance Bmc Neuroscience. 13. DOI: 10.1186/1471-2202-13-S1-P44 |
0.666 |
|
| 2011 |
Goldwyn JH, Shea-Brown E. The what and where of adding channel noise to the Hodgkin-Huxley equations. Plos Computational Biology. 7: e1002247. PMID 22125479 DOI: 10.1371/Journal.Pcbi.1002247 |
0.765 |
|
| 2011 |
Goldwyn JH, Imennov NS, Famulare M, Shea-Brown E. Stochastic differential equation models for ion channel noise in Hodgkin-Huxley neurons. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 83: 041908. PMID 21599202 DOI: 10.1103/Physreve.83.041908 |
0.779 |
|
| 2011 |
Matell MS, Shea-Brown E, Gooch C, Wilson AG, Rinzel J. A heterogeneous population code for elapsed time in rat medial agranular cortex. Behavioral Neuroscience. 125: 54-73. PMID 21319888 DOI: 10.1037/A0021954 |
0.713 |
|
| 2011 |
Lajoie G, Shea-Brown E. Shared inputs, entrainment, and desynchrony in elliptic bursters: From slow passage to discontinuous circle maps Siam Journal On Applied Dynamical Systems. 10: 1232-1271. DOI: 10.1137/100811726 |
0.645 |
|
| 2011 |
Goldwyn JH, Imennov NS, Famulare M, Shea-Brown E. Publisher’s Note: Stochastic differential equation models for ion channel noise in Hodgkin-Huxley neurons [Phys. Rev. E83, 041908 (2011)] Physical Review E. 83. DOI: 10.1103/Physreve.83.049902 |
0.774 |
|
| 2010 |
Barreiro AK, Shea-Brown E, Thilo EL. Time scales of spike-train correlation for neural oscillators with common drive. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 81: 011916. PMID 20365408 DOI: 10.1103/Physreve.81.011916 |
0.754 |
|
| 2010 |
Goldwyn JH, Shea-Brown E, Rubinstein JT. Encoding and decoding amplitude-modulated cochlear implant stimuli--a point process analysis. Journal of Computational Neuroscience. 28: 405-24. PMID 20177761 DOI: 10.1007/S10827-010-0224-9 |
0.788 |
|
| 2010 |
Cain N, Barreiro A, Shadlen M, Shea-Brown E. The effect of “robust” integrator dynamics on decision-making performance Bmc Neuroscience. 11. DOI: 10.1186/1471-2202-11-S1-P97 |
0.801 |
|
| 2010 |
Barreiro AK, Shea-Brown ET, Rieke FM, Gjorgjieva J. When are microcircuits well-modeled by maximum entropy methods? Bmc Neuroscience. 11. DOI: 10.1186/1471-2202-11-S1-P65 |
0.808 |
|
| 2010 |
Goldwyn JH, Shea-Brown E. Adaptation in electric hearing: analysis of level and amplitude modulation encoding Bmc Neuroscience. 11. DOI: 10.1186/1471-2202-11-S1-P166 |
0.799 |
|
| 2009 |
Josić K, Shea-Brown E, Doiron B, de la Rocha J. Stimulus-dependent correlations and population codes. Neural Computation. 21: 2774-804. PMID 19635014 DOI: 10.1162/Neco.2009.10-08-879 |
0.796 |
|
| 2009 |
Lin KK, Shea-Brown E, Young LS. Spike-time reliability of layered neural oscillator networks. Journal of Computational Neuroscience. 27: 135-60. PMID 19156509 DOI: 10.1007/S10827-008-0133-3 |
0.417 |
|
| 2009 |
Lin KK, Shea-Brown E, Young LS. Reliability of layered neural oscillator networks Communications in Mathematical Sciences. 7: 239-247. DOI: 10.4310/Cms.2009.V7.N1.A12 |
0.304 |
|
| 2009 |
Lin KK, Shea-Brown E, Young LS. Reliability of coupled oscillators Journal of Nonlinear Science. 19: 497-545. DOI: 10.1007/S00332-009-9042-5 |
0.344 |
|
| 2008 |
Shea-Brown E, Gilzenrat MS, Cohen JD. Optimization of decision making in multilayer networks: the role of locus coeruleus. Neural Computation. 20: 2863-94. PMID 18624653 DOI: 10.1162/Neco.2008.03-07-487 |
0.369 |
|
| 2008 |
Shea-Brown E, Josić K, de la Rocha J, Doiron B. Correlation and synchrony transfer in integrate-and-fire neurons: basic properties and consequences for coding. Physical Review Letters. 100: 108102. PMID 18352234 DOI: 10.1103/Physrevlett.100.108102 |
0.808 |
|
| 2008 |
Goldwyn JH, Shea-Brown E. Amplitude modulation discrimination in a model of the electrically stimulated auditory nerve Bmc Neuroscience. 9. DOI: 10.1186/1471-2202-9-S1-P119 |
0.761 |
|
| 2007 |
de la Rocha J, Doiron B, Shea-Brown E, Josić K, Reyes A. Correlation between neural spike trains increases with firing rate. Nature. 448: 802-6. PMID 17700699 DOI: 10.1038/Nature06028 |
0.814 |
|
| 2007 |
Feng XJ, Shea-Brown E, Greenwald B, Kosut R, Rabitz H. Optimal deep brain stimulation of the subthalamic nucleus--a computational study. Journal of Computational Neuroscience. 23: 265-82. PMID 17484043 DOI: 10.1007/S10827-007-0031-0 |
0.376 |
|
| 2006 |
Coombes S, Doiron B, Josić K, Shea-Brown E. Towards blueprints for network architecture, biophysical dynamics and signal transduction. Philosophical Transactions. Series a, Mathematical, Physical, and Engineering Sciences. 364: 3301-18. PMID 17090461 DOI: 10.1098/Rsta.2006.1903 |
0.653 |
|
| 2006 |
Shea-Brown E, Rinzel J, Rakitin BC, Malapani C. A firing rate model of Parkinsonian deficits in interval timing. Brain Research. 1070: 189-201. PMID 16413510 DOI: 10.1016/J.Brainres.2005.10.070 |
0.701 |
|
| 2006 |
Moehlis J, Shea-Brown E, Rabitz H. Optimal inputs for phase models of spiking neurons Journal of Computational and Nonlinear Dynamics. 1: 358-367. DOI: 10.1115/1.2338654 |
0.465 |
|
| 2006 |
Golubitsky M, Josić K, Shea-Brown E. Winding numbers and average frequencies in phase oscillator networks Journal of Nonlinear Science. 16: 201-231. DOI: 10.1007/S00332-005-0696-3 |
0.307 |
|
| 2005 |
Holmes P, Shea-Brown E, Moehlis J, Bogacz R, Gao J, Aston-Jones G, Clayton E, Rajkowski J, Cohen JD. Optimal decisions: From neural spikes, through stochastic differential equations, to behavior Ieice Transactions On Fundamentals of Electronics, Communications and Computer Sciences. 2496-2502. DOI: 10.1093/Ietfec/E88-A.10.2496 |
0.628 |
|
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