Eric Shea-Brown

According to our database, Eric Shea-Brown authored at least 54 papers between 2004 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

A biologically inspired architecture with switching units can learn to generalize across backgrounds.

Doris Voina, Eric Shea-Brown, Stefan Mihalas

Neural Networks, November, 2023

Modeling functional cell types in spike train data.

Daniel N. Zdeblick, Eric Shea-Brown, Daniela M. Witten, Michael A. Buice

PLoS Comput. Biol., October, 2023

Heterogeneity in Neuronal Dynamics Is Learned by Gradient Descent for Temporal Processing Tasks.

Chloe N. Winston, Dana Mastrovito, Eric Shea-Brown, Stefan Mihalas

Neural Comput., 2023

Evolutionary algorithms as an alternative to backpropagation for supervised training of Biophysical Neural Networks and Neural ODEs.

James Hazelden, Yuhan Helena Liu, Eli Shlizerman, Eric Shea-Brown

CoRR, 2023

Attention for Causal Relationship Discovery from Biological Neural Dynamics.

Ziyu Lu, Anika Tabassum, Shruti R. Kulkarni, Lu Mi, J. Nathan Kutz, Eric Shea-Brown, Seung-Hwan Lim

CoRR, 2023

How connectivity structure shapes rich and lazy learning in neural circuits.

Yuhan Helena Liu, Aristide Baratin, Jonathan Cornford, Stefan Mihalas, Eric Shea-Brown, Guillaume Lajoie

CoRR, 2023

A simple connection from loss flatness to compressed representations in neural networks.

Shirui Chen, Stefano Recanatesi, Eric Shea-Brown

CoRR, 2023

Expressive probabilistic sampling in recurrent neural networks.

Shirui Chen, Linxing Preston Jiang, Rajesh P. N. Rao, Eric Shea-Brown

CoRR, 2023

Expressive probabilistic sampling in recurrent neural networks.

Shirui Chen, Linxing Jiang, Rajesh P. N. Rao, Eric Shea-Brown

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Author Correction: Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion.

Matthew Farrell, Stefano Recanatesi, Timothy Moore, Guillaume Lajoie, Eric Shea-Brown

Nat. Mac. Intell., November, 2022

MouseNet: A biologically constrained convolutional neural network model for the mouse visual cortex.

Jianghong Shi, Bryan P. Tripp, Eric Shea-Brown, Stefan Mihalas, Michael A. Buice

PLoS Comput. Biol., September, 2022

A scale-dependent measure of system dimensionality.

Stefano Recanatesi, Serena Bradde, Vijay Balasubramanian, Nicholas A. Steinmetz, Eric Shea-Brown

Patterns, 2022

Single Circuit in V1 Capable of Switching Contexts During Movement Using an Inhibitory Population as a Switch.

Doris Voina, Stefano Recanatesi, Brian Hu, Eric Shea-Brown, Stefan Mihalas

Neural Comput., 2022

Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion.

Matthew Farrell, Stefano Recanatesi, Timothy Moore, Guillaume Lajoie, Eric Shea-Brown

Nat. Mach. Intell., 2022

Learning dynamics of deep linear networks with multiple pathways.

Jianghong Shi, Eric Shea-Brown, Michael A. Buice

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators.

Yuhan Helena Liu, Stephen Smith, Stefan Mihalas, Eric Shea-Brown, Uygar Sümbül

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules.

Yuhan Helena Liu, Arna Ghosh, Blake A. Richards, Eric Shea-Brown, Guillaume Lajoie

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Autoencoder networks extract latent variables and encode these variables in their connectomes.

Matthew Farrell, Stefano Recanatesi, R. Clay Reid, Stefan Mihalas, Eric Shea-Brown

Neural Networks, 2021

2019

Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity.

Stefano Recanatesi, Gabriel Koch Ocker, Michael A. Buice, Eric Shea-Brown

PLoS Comput. Biol., 2019

Dimensionality compression and expansion in Deep Neural Networks.

Stefano Recanatesi, Matthew Farrell, Madhu Advani, Timothy Moore, Guillaume Lajoie, Eric Shea-Brown

CoRR, 2019

Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex.

Jianghong Shi, Eric Shea-Brown, Michael A. Buice

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Predicting how and when hidden neurons skew measured synaptic interactions.

Braden A. W. Brinkman, Fred Rieke, Eric Shea-Brown, Michael A. Buice

PLoS Comput. Biol., 2018

Predictive Coding in Area V4: Dynamic Shape Discrimination under Partial Occlusion.

Hannah Choi, Anitha Pasupathy, Eric Shea-Brown

Neural Comput., 2018

A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural Data.

N. Alex Cayco-Gajic, Joel Zylberberg, Eric Shea-Brown

Entropy, 2018

2017

Robust information propagation through noisy neural circuits.

Joel Zylberberg, Alexandre Pouget, Peter E. Latham, Eric Shea-Brown

PLoS Comput. Biol., 2017

Linking structure and activity in nonlinear spiking networks.

Gabriel Koch Ocker, Kresimir Josic, Eric Shea-Brown, Michael A. Buice

PLoS Comput. Biol., 2017

2016

Encoding in Balanced Networks: Revisiting Spike Patterns and Chaos in Stimulus-Driven Systems.

Guillaume Lajoie, Kevin K. Lin, Jean-Philippe Thivierge, Eric Shea-Brown

PLoS Comput. Biol., 2016

How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?

Braden A. W. Brinkman, Alison I. Weber, Fred Rieke, Eric Shea-Brown

PLoS Comput. Biol., 2016

High resolution neural connectivity from incomplete tracing data using nonnegative spline regression.

Kameron Decker Harris, Stefan Mihalas, Eric Shea-Brown

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Triplet correlations among similarly tuned cells impact population coding.

Natasha A. Cayco-Gajic, Joel Zylberberg, Eric Shea-Brown

Frontiers Comput. Neurosci., 2015

2014

The Sign Rule and Beyond: Boundary Effects, Flexibility, and Noise Correlations in Neural Population Codes.

Yu Hu, Joel Zylberberg, Eric Shea-Brown

PLoS Comput. Biol., 2014

Structured chaos shapes spike-response noise entropy in balanced neural networks.

Guillaume Lajoie, Jean-Philippe Thivierge, Eric Shea-Brown

Frontiers Comput. Neurosci., 2014

When do microcircuits produce beyond-pairwise correlations?

Andrea K. Barreiro, Julijana Gjorgjieva, Fred Rieke, Eric Shea-Brown

Frontiers Comput. Neurosci., 2014

2013

Neutral Stability, Rate Propagation, and Critical Branching in Feedforward Networks.

N. Alex Cayco-Gajic, Eric Shea-Brown

Neural Comput., 2013

Impact of Correlated Neural Activity on Decision-Making Performance.

Nicholas Cain, Eric Shea-Brown

Neural Comput., 2013

A generative spike train model with time-structured higher order correlations.

James Trousdale, Yu Hu, Eric Shea-Brown, Kresimir Josic

Frontiers Comput. Neurosci., 2013

2012

Impact of Network Structure and Cellular Response on Spike Time Correlations.

James Trousdale, Yu Hu, Eric Shea-Brown, Kresimir Josic

PLoS Comput. Biol., 2012

2011

Shared Inputs, Entrainment, and Desynchrony in Elliptic Bursters: From Slow Passage to Discontinuous Circle Maps.

Guillaume Lajoie, Eric Shea-Brown

SIAM J. Appl. Dyn. Syst., 2011

The What and Where of Adding Channel Noise to the Hodgkin-Huxley Equations.

Joshua H. Goldwyn, Eric Shea-Brown

PLoS Comput. Biol., 2011

2010

Encoding and decoding amplitude-modulated cochlear implant stimuli - a point process analysis.

Joshua H. Goldwyn, Eric Shea-Brown, Jay T. Rubinstein

J. Comput. Neurosci., 2010

When are feedforward microcircuits well-modeled by maximum entropy methods?

Andrea K. Barreiro, Julijana Gjorgjieva, Fred Rieke, Eric Shea-Brown

CoRR, 2010

2009

Stimulus-Dependent Correlations and Population Codes.

Kresimir Josic, Eric Shea-Brown, Brent Doiron, Jaime de la Rocha

Neural Comput., 2009

Reliability of Coupled Oscillators.

Kevin K. Lin, Eric Shea-Brown, Lai-Sang Young

J. Nonlinear Sci., 2009

Spike-time reliability of layered neural oscillator networks.

Kevin K. Lin, Eric Shea-Brown, Lai-Sang Young

J. Comput. Neurosci., 2009

2008

Optimization of Decision Making in Multilayer Networks: The Role of Locus Coeruleus.

Eric Shea-Brown, Mark S. Gilzenrat, Jonathan D. Cohen

Neural Comput., 2008

2007

Optimal deep brain stimulation of the subthalamic nucleus - a computational study.

Xiao-Jiang Feng, Eric Shea-Brown, Brian Greenwald, Robert L. Kosut, Herschel Rabitz

J. Comput. Neurosci., 2007

2006

Periodic orbit.

Jeff Moehlis, Kresimir Josic, Eric Shea-Brown

Scholarpedia, 2006

Isochron.

Kresimir Josic, Eric Shea-Brown, Jeff Moehlis

Scholarpedia, 2006

Stability.

Philip Holmes, Eric Shea-Brown

Scholarpedia, 2006

Winding Numbers and Average Frequencies in Phase Oscillator Networks.

Martin Golubitsky, Kresimir Josic, Eric Shea-Brown

J. Nonlinear Sci., 2006

2005

Simple Neural Networks that Optimize Decisions.

Eric Brown, Juan Gao, Philip Holmes, Rafal Bogacz, Mark S. Gilzenrat, Jonathan D. Cohen

Int. J. Bifurc. Chaos, 2005

Optimal Decisions: From Neural Spikes, through Stochastic Differential Equations, to Behavior.

Philip Holmes, Eric Shea-Brown, Jeff Moehlis, Rafal Bogacz, Juan Gao, Gary Aston-Jones, Ed Clayton, Janusz Rajkowski, Jonathan D. Cohen

IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2005

2004

On the Phase Reduction and Response Dynamics of Neural Oscillator Populations.

Eric Brown, Jeff Moehlis, Philip Holmes

Neural Comput., 2004

The Influence of Spike Rate and Stimulus Duration on Noradrenergic Neurons.

Eric Brown, Jeff Moehlis, Philip Holmes, Ed Clayton, Janusz Rajkowski, Gary Aston-Jones

J. Comput. Neurosci., 2004