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Eric Shea-Brown
Person information
- affiliation: University of Washington, Seattle, WA, USA
- affiliation (former): New York University, NY, USA
- affiliation (former): Princeton University, NJ, USA
Other persons with the same name
- Eric Brown — disambiguation page
- Eric Brown 0001 — The University of Chicago, IL, USA
- Eric Brown 0003 — Syracuse Research Corporation, North Syracuse, NY, USA
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2020 – today
- 2024
- [c7]Yuhan Helena Liu, Aristide Baratin, Jonathan Cornford, Stefan Mihalas, Eric Shea-Brown, Guillaume Lajoie:
How connectivity structure shapes rich and lazy learning in neural circuits. ICLR 2024 - 2023
- [j41]Chloe N. Winston, Dana Mastrovito, Eric Shea-Brown, Stefan Mihalas:
Heterogeneity in Neuronal Dynamics Is Learned by Gradient Descent for Temporal Processing Tasks. Neural Comput. 35(4): 555-592 (2023) - [j40]Doris Voina
, Eric Shea-Brown, Stefan Mihalas:
A biologically inspired architecture with switching units can learn to generalize across backgrounds. Neural Networks 168: 615-630 (2023) - [j39]Daniel N. Zdeblick
Modeling functional cell types in spike train data. PLoS Comput. Biol. 19(10) (2023) - [c6]Shirui Chen, Linxing Jiang, Rajesh P. N. Rao, Eric Shea-Brown:
Expressive probabilistic sampling in recurrent neural networks. NeurIPS 2023 - [i9]Shirui Chen, Linxing Preston Jiang, Rajesh P. N. Rao, Eric Shea-Brown:
Expressive probabilistic sampling in recurrent neural networks. CoRR abs/2308.11809 (2023) - [i8]Shirui Chen, Stefano Recanatesi
A simple connection from loss flatness to compressed representations in neural networks. CoRR abs/2310.01770 (2023) - [i7]Yuhan Helena Liu, Aristide Baratin, Jonathan Cornford, Stefan Mihalas, Eric Shea-Brown, Guillaume Lajoie:
How connectivity structure shapes rich and lazy learning in neural circuits. CoRR abs/2310.08513 (2023) - [i6]Ziyu Lu, Anika Tabassum, Shruti R. Kulkarni, Lu Mi, J. Nathan Kutz, Eric Shea-Brown, Seung-Hwan Lim:
Attention for Causal Relationship Discovery from Biological Neural Dynamics. CoRR abs/2311.06928 (2023) - [i5]James Hazelden, Yuhan Helena Liu, Eli Shlizerman, Eric Shea-Brown:
Evolutionary algorithms as an alternative to backpropagation for supervised training of Biophysical Neural Networks and Neural ODEs. CoRR abs/2311.10869 (2023) - 2022
- [j38]Matthew Farrell
Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion. Nat. Mach. Intell. 4(6): 564-573 (2022) - [j37]Matthew Farrell
Author Correction: Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion. Nat. Mac. Intell. 4(11): 1053 (2022) - [j36]Doris Voina, Stefano Recanatesi
Single Circuit in V1 Capable of Switching Contexts During Movement Using an Inhibitory Population as a Switch. Neural Comput. 34(3): 541-594 (2022) - [j35]Stefano Recanatesi
A scale-dependent measure of system dimensionality. Patterns 3(8): 100555 (2022) - [j34]Jianghong Shi
MouseNet: A biologically constrained convolutional neural network model for the mouse visual cortex. PLoS Comput. Biol. 18(9): 1010427 (2022) - [c5]Yuhan Helena Liu, Arna Ghosh, Blake A. Richards, Eric Shea-Brown, Guillaume Lajoie:
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules. NeurIPS 2022 - [c4]Yuhan Helena Liu, Stephen Smith, Stefan Mihalas, Eric Shea-Brown, Uygar Sümbül:
Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators. NeurIPS 2022 - [c3]Jianghong Shi, Eric Shea-Brown, Michael A. Buice:
Learning dynamics of deep linear networks with multiple pathways. NeurIPS 2022 - [i4]Yuhan Helena Liu, Arna Ghosh, Blake A. Richards, Eric Shea-Brown, Guillaume Lajoie:
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules. CoRR abs/2206.00823 (2022) - [i3]Yuhan Helena Liu, Stephen Smith, Stefan Mihalas, Eric Shea-Brown, Uygar Sümbül:
Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators. CoRR abs/2206.01338 (2022) - 2021
- [j33]Matthew Farrell
Autoencoder networks extract latent variables and encode these variables in their connectomes. Neural Networks 141: 330-343 (2021)
2010 – 2019
- 2019
- [j32]Stefano Recanatesi
Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity. PLoS Comput. Biol. 15(7) (2019) - [c2]Jianghong Shi, Eric Shea-Brown, Michael A. Buice:
Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex. NeurIPS 2019: 5765-5775 - [i2]Stefano Recanatesi, Matthew Farrell
Dimensionality compression and expansion in Deep Neural Networks. CoRR abs/1906.00443 (2019) - 2018
- [j31]N. Alex Cayco-Gajic
A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural Data. Entropy 20(7): 489 (2018) - [j30]Hannah Choi
Predictive Coding in Area V4: Dynamic Shape Discrimination under Partial Occlusion. Neural Comput. 30(5) (2018) - [j29]Braden A. W. Brinkman
Predicting how and when hidden neurons skew measured synaptic interactions. PLoS Comput. Biol. 14(10) (2018) - 2017
- [j28]Joel Zylberberg, Alexandre Pouget
Robust information propagation through noisy neural circuits. PLoS Comput. Biol. 13(4) (2017) - [j27]Gabriel Koch Ocker
Linking structure and activity in nonlinear spiking networks. PLoS Comput. Biol. 13(6) (2017) - 2016
- [j26]Braden A. W. Brinkman
How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits? PLoS Comput. Biol. 12(10) (2016) - [j25]Guillaume Lajoie, Kevin K. Lin
Encoding in Balanced Networks: Revisiting Spike Patterns and Chaos in Stimulus-Driven Systems. PLoS Comput. Biol. 12(12) (2016) - [c1]Kameron Decker Harris, Stefan Mihalas, Eric Shea-Brown:
High resolution neural connectivity from incomplete tracing data using nonnegative spline regression. NIPS 2016: 3099-3107 - 2015
- [j24]Natasha A. Cayco-Gajic
Triplet correlations among similarly tuned cells impact population coding. Frontiers Comput. Neurosci. 9: 57 (2015) - 2014
- [j23]Andrea K. Barreiro, Julijana Gjorgjieva
When do microcircuits produce beyond-pairwise correlations? Frontiers Comput. Neurosci. 8: 10 (2014) - [j22]Guillaume Lajoie, Jean-Philippe Thivierge, Eric Shea-Brown:
Structured chaos shapes spike-response noise entropy in balanced neural networks. Frontiers Comput. Neurosci. 8: 123 (2014) - [j21]Yu Hu, Joel Zylberberg, Eric Shea-Brown:
The Sign Rule and Beyond: Boundary Effects, Flexibility, and Noise Correlations in Neural Population Codes. PLoS Comput. Biol. 10(2) (2014) - 2013
- [j20]James Trousdale, Yu Hu, Eric Shea-Brown, Kresimir Josic:
A generative spike train model with time-structured higher order correlations. Frontiers Comput. Neurosci. 7: 84 (2013) - [j19]Nicholas Cain, Eric Shea-Brown:
Impact of Correlated Neural Activity on Decision-Making Performance. Neural Comput. 25(2): 289-327 (2013) - [j18]N. Alex Cayco-Gajic
Neutral Stability, Rate Propagation, and Critical Branching in Feedforward Networks. Neural Comput. 25(7): 1768-1806 (2013) - 2012
- [j17]James Trousdale, Yu Hu, Eric Shea-Brown, Kresimir Josic:
Impact of Network Structure and Cellular Response on Spike Time Correlations. PLoS Comput. Biol. 8(3) (2012) - 2011
- [j16]Joshua H. Goldwyn
The What and Where of Adding Channel Noise to the Hodgkin-Huxley Equations. PLoS Comput. Biol. 7(11) (2011) - [j15]Guillaume Lajoie, Eric Shea-Brown:
Shared Inputs, Entrainment, and Desynchrony in Elliptic Bursters: From Slow Passage to Discontinuous Circle Maps. SIAM J. Appl. Dyn. Syst. 10(4): 1232-1271 (2011) - 2010
- [j14]Joshua H. Goldwyn
Encoding and decoding amplitude-modulated cochlear implant stimuli - a point process analysis. J. Comput. Neurosci. 28(3): 405-424 (2010) - [i1]Andrea K. Barreiro, Julijana Gjorgjieva, Fred Rieke, Eric Shea-Brown:
When are feedforward microcircuits well-modeled by maximum entropy methods? CoRR abs/1011.2797 (2010)
2000 – 2009
- 2009
- [j13]Kevin K. Lin
Spike-time reliability of layered neural oscillator networks. J. Comput. Neurosci. 27(1): 135-160 (2009) - [j12]Kevin K. Lin
Reliability of Coupled Oscillators. J. Nonlinear Sci. 19(5): 497-545 (2009) - [j11]Kresimir Josic, Eric Shea-Brown, Brent Doiron, Jaime de la Rocha
Stimulus-Dependent Correlations and Population Codes. Neural Comput. 21(10): 2774-2804 (2009) - 2008
- [j10]Eric Shea-Brown, Mark S. Gilzenrat, Jonathan D. Cohen:
Optimization of Decision Making in Multilayer Networks: The Role of Locus Coeruleus. Neural Comput. 20(12): 2863-2894 (2008) - 2007
- [j9]Xiao-Jiang Feng, Eric Shea-Brown, Brian Greenwald, Robert L. Kosut, Herschel Rabitz
Optimal deep brain stimulation of the subthalamic nucleus - a computational study. J. Comput. Neurosci. 23(3): 265-282 (2007) - 2006
- [j8]Martin Golubitsky, Kresimir Josic, Eric Shea-Brown:
Winding Numbers and Average Frequencies in Phase Oscillator Networks. J. Nonlinear Sci. 16(3): 201-231 (2006) - [j7]Jeff Moehlis, Kresimir Josic, Eric Shea-Brown:
Periodic orbit. Scholarpedia 1(7): 1358 (2006) - [j6]Kresimir Josic, Eric Shea-Brown, Jeff Moehlis:
Isochron. Scholarpedia 1(8): 1361 (2006) - [j5]Philip Holmes, Eric Shea-Brown:
Stability. Scholarpedia 1(10): 1838 (2006) - 2005
- [j4]Philip Holmes, Eric Shea-Brown, Jeff Moehlis, Rafal Bogacz, Juan Gao, Gary Aston-Jones, Ed Clayton, Janusz Rajkowski, Jonathan D. Cohen:
Optimal Decisions: From Neural Spikes, through Stochastic Differential Equations, to Behavior. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 88-A(10): 2496-2503 (2005) - [j3]Eric Brown, Juan Gao, Philip Holmes, Rafal Bogacz, Mark S. Gilzenrat, Jonathan D. Cohen:
Simple Neural Networks that Optimize Decisions. Int. J. Bifurc. Chaos 15(3): 803-826 (2005) - 2004
- [j2]Eric Brown, Jeff Moehlis, Philip Holmes, Ed Clayton, Janusz Rajkowski, Gary Aston-Jones:
The Influence of Spike Rate and Stimulus Duration on Noradrenergic Neurons. J. Comput. Neurosci. 17(1): 13-29 (2004) - [j1]Eric Brown, Jeff Moehlis, Philip Holmes:
On the Phase Reduction and Response Dynamics of Neural Oscillator Populations. Neural Comput. 16(4): 673-715 (2004)
Coauthor Index
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last updated on 2024-11-11 21:29 CET by the dblp team
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