Eric Shea-Brown

发表

Optimal deep brain stimulation of the subthalamic nucleus—a computational study

Xiao-Jiang Feng, Eric Shea-Brown, Brian Greenwald, 2007, Journal of Computational Neuroscience.

The effect of “robust” integrator dynamics on decision-making performance

Eric Shea-Brown, Nicholas Cain, Andrea Barreiro, 2010, BMC Neuroscience.

High-resolution data-driven model of the mouse connectome

Eric Shea-Brown, Stefan Mihalas, Joseph E Knox, 2018, bioRxiv.

On the Phase Reduction and Response Dynamics of Neural Oscillator Populations

Eric Shea-Brown, Philip Holmes, Jeff Moehlis, 2004, Neural Computation.

Inferring the Spiking Rate of a Population of Neurons from Wide-Field Calcium Imaging

Eric Shea-Brown, Daniela Witten, Merav Stern, 2020, bioRxiv.

Dynamic compression and expansion in a classifying recurrent network

Eric Shea-Brown, Matthew Farrell, Stefano Recanatesi, 2019, bioRxiv.

Identification of Multiple Noise Sources Improves Estimation of Neural Responses across Stimulus Conditions

Eric Shea-Brown, Fred Rieke, Alison I. Weber, 2020, eNeuro.

Dimensionality compression and expansion in Deep Neural Networks pdf

Eric Shea-Brown, Matthew Farrell, Timothy Moore, 2019, ArXiv.

Simple Neural Networks that Optimize Decisions

Philip Holmes, Eric Shea-Brown, Jonathan D. Cohen, 2005, Int. J. Bifurc. Chaos.

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

Yu Hu, Eric Shea-Brown, Joel Zylberberg, 2013, PLoS Comput. Biol..

25th Annual Computational Neuroscience Meeting: CNS-2016

Morten L. Kringelbach, Majid Nili Ahmadabadi, Anders M. Dale, 2016, BMC Neuroscience.

Eric Shea-Brown, Nicholas Cain, Eric T. Shea-Brown, 2012, Neural Computation.

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

Philip Holmes, Eric Shea-Brown, Jonathan D. Cohen, 2005, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

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

Eric Shea-Brown, N. Alex Cayco-Gajic, Joel Zylberberg, 2018 .

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

Eric Shea-Brown, Jianghong Shi, Michael A. Buice, 2019, NeurIPS.

When do microcircuits produce beyond-pairwise correlations?

Eric Shea-Brown, Julijana Gjorgjieva, Andrea K. Barreiro, 2014, Front. Comput. Neurosci..

Predicting how and when hidden neurons skew measured synaptic interactions

Eric Shea-Brown, Fred Rieke, Braden A. W. Brinkman, 2017, bioRxiv.

A simple mechanism for higher-order correlations in integrate-and-fire neurons

Eric Shea-Brown, David A Leen, 2012, BMC Neuroscience.

High resolution data-driven model of the mouse connectome

Eric Shea-Brown, Stefan Mihalas, Kameron Decker Harris, 2018, bioRxiv.

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

Eric Shea-Brown, Stefan Mihalas, Matthew Farrell, 2020, bioRxiv.

Local paths to global coherence: cutting networks down to size.

Eric Shea-Brown, Krešimir Josić, Yu Hu, 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

The Influence of Spike Rate and Stimulus Duration on Noradrenergic Neurons

Philip Holmes, Eric Shea-Brown, Jeff Moehlis, 2004, Journal of Computational Neuroscience.

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

Eric Shea-Brown, Jay T. Rubinstein, Joshua H. Goldwyn, 2010, Journal of Computational Neuroscience.

Predicting how and when hidden neurons skew measured synaptic interactions

Eric Shea-Brown, Michael A. Buice, Fred Rieke, 2017 .

Winding Numbers and Average Frequencies in Phase Oscillator Networks

Eric Shea-Brown, Martin Golubitsky, Kresimir Josic, 2006, J. Nonlinear Sci..

A point process framework for modeling electrical stimulation of the auditory nerve.

Eric Shea-Brown, Joshua H. Goldwyn, Joshua H Goldwyn, 2012, Journal of neurophysiology.

Speed and accuracy in decision making: input correlations and performance

Eric Shea-Brown, Nicholas Cain, 2012, BMC Neuroscience.

From the statistics of connectivity to the statistics of spike times in neuronal networks

Eric Shea-Brown, Brent Doiron, Michael A Buice, 2017, Current Opinion in Neurobiology.

Linking structure and activity in nonlinear spiking networks

Eric Shea-Brown, Kresimir Josic, Michael A. Buice, 2016, bioRxiv.

When does recurrent connectivity improve neural population coding?

Eric Shea-Brown, Joel Zylberberg, 2014, BMC Neuroscience.

Structured chaos shapes joint spike-response noise entropy in temporally driven balanced networks

Eric Shea-Brown, Jean-Philippe Thivierge, Guillaume Lajoie, 2014, BMC Neuroscience.

Linking structure and activity in nonlinear spiking networks

Eric Shea-Brown, Krešimir Josić, Michael A. Buice, 2016 .

Periodic orbit

Eric Shea-Brown, Jeff Moehlis, Kresimir Josic, 2006, Scholarpedia.

Poster: How Connectivity Motifs Can Shape Signal Filtering in Recurrent Neural Networks

Steven L. Brunton, Eric Shea-Brown, J. Nathan Kutz, 2014 .

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

Eric Shea-Brown, Julijana Gjorgjieva, Andrea K. Barreiro, 2010, ArXiv.

Computational models of decision making: integration, stability, and noise

Eric Shea-Brown, Nicholas Cain, Eric T. Shea-Brown, 2012, Current Opinion in Neurobiology.

Signatures and mechanisms of low-dimensional neural predictive manifolds

Eric Shea-Brown, Matthew Farrell, Mattia Rigotti, 2018, bioRxiv.

Strong and localized recurrence controls dimensionality of neural activity across brain areas

Eric Shea-Brown, Moritz Helias, David Dahmen, 2020, bioRxiv.

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

Eric Shea-Brown, Stefano Recanatesi, Michael A Buice, 2018, bioRxiv.

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

Eric Shea-Brown, Jonathan D. Cohen, Mark S. Gilzenrat, 2008, Neural Computation.

A Convolutional Network Architecture Driven by Mouse Neuroanatomical Data

Eric Shea-Brown, Stefan Mihalas, Jianghong Shi, 2020, bioRxiv.

A solution to temporal credit assignment using cell-type-specific modulatory signals

Eric Shea-Brown, Stefan Mihalas, Uygar Sümbül, 2020, bioRxiv.

Input nonlinearities can shape beyond-pairwise correlations and improve information transmission by neural populations.

Eric Shea-Brown, Joel Zylberberg, Eric T. Shea-Brown, 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

Consistency requirements determine optimal noise correlations in neural populations

Eric Shea-Brown, Fred Rieke, Joel Zylberberg, 2013, BMC Neuroscience.

Chaos and reliability in balanced spiking networks with temporal drive.

Eric Shea-Brown, Guillaume Lajoie, Kevin K Lin, 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

Noise- and stimulus-dependence of the optimal encoding nonlinearities in a simple ON/OFF retinal circuit model

Eric Shea-Brown, Fred Rieke, Braden A W Brinkman, 2014, BMC Neuroscience.

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

Eric Shea-Brown, Fred Rieke, Alison I. Weber, 2016, PLoS Comput. Biol..

Single circuit in V1 capable of switching contexts during movement using VIP population as a switch

Eric Shea-Brown, Stefan Mihalas, Stefano Recanatesi, 2020 .

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

Eric Shea-Brown, Joshua H. Goldwyn, Eric T. Shea-Brown, 2011, PLoS Comput. Biol..

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

Eric Shea-Brown, Michael A. Buice, Stefano Recanatesi, 2019, PLoS Comput. Biol..

Differential roles for inhibition in excitatory rhythm generators

Eric Shea-Brown, Kameron Decker Harris, Jan-Marino Ramirez, 2016 .

Predictive learning extracts latent space representations from sensory observations

Eric Shea-Brown, Sophie Denève, Matthew Farrell, 2019 .

Transformation of population code from dLGN to V1 facilitates linear decoding

Eric Shea-Brown, Ramakrishnan Iyer, Michael Buice, 2019, bioRxiv.

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

Eric Shea-Brown, Jean-Philippe Thivierge, Guillaume Lajoie, 2016, PLoS Comput. Biol..

Network Dynamics Governed by Lyapunov Functions: From Memory to Classification

Eric Shea-Brown, Merav Stern, Eric T. Shea-Brown, 2020, Trends in Neurosciences.

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

Eric Shea-Brown, Jean-Philippe Thivierge, Guillaume Lajoie, 2014, Front. Comput. Neurosci..

Reliability of Coupled Oscillators

Eric Shea-Brown, Lai-Sang Young, Kevin K. Lin, 2009, J. Nonlinear Sci..

Stimulus-Dependent Correlations and Population Codes

Eric Shea-Brown, Brent Doiron, Kresimir Josic, 2008, Neural Computation.

Robust information propagation through noisy neural circuits

Peter E. Latham, Alexandre Pouget, Eric Shea-Brown, 2016, PLoS Comput. Biol..

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

Eric Shea-Brown, Kresimir Josic, Yu Hu, 2013, Front. Comput. Neurosci..

Predictive learning as a network mechanism for extracting low-dimensional latent space representations

Eric Shea-Brown, Matthew Farrell, Mattia Rigotti, 2021, Nature Communications.

Neural integrators for decision making: a favorable tradeoff between robustness and sensitivity

Eric Shea-Brown, Nicholas Cain, Andrea K. Barreiro, 2011, Journal of neurophysiology.

Spike-time reliability of layered neural oscillator networks

Eric Shea-Brown, Lai-Sang Young, Kevin K. Lin, 2009, Journal of Computational Neuroscience.

Feedback through graph motifs relates structure and function in complex networks

Steven L. Brunton, Eric Shea-Brown, Nicholas Cain, 2016, Physical Review E.

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

Eric Shea-Brown, N. Alex Cayco-Gajic, E. Shea-Brown, 2012, Neural Computation.

Effective synaptic interactions in subsampled nonlinear networks with strong coupling

Eric Shea-Brown, Michael A. Buice, Fred Rieke, 2017 .

Chaos and reliability in balanced spiking networks

Eric Shea-Brown, Guillaume Lajoie, Kevin K. Lin, 2012 .

Computational Neuroscience: Mathematical and Statistical Perspectives.

Sonja Grün, Tomoki Fukai, Eric Shea-Brown, 2018, Annual review of statistics and its application.

Triplet correlations among similarly tuned cells impact population coding

Eric Shea-Brown, Joel Zylberberg, Eric T. Shea-Brown, 2015, Front. Comput. Neurosci..

Nonlinear convergence preserves information

Eric Shea-Brown, Gabrielle J Gutierrez, Fred Rieke, 2019 .

Reliability of Layered Neural Oscillator Networks

Eric Shea-Brown, Lai-Sang Young, Kevin K. Lin, 2008 .

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

Eric Shea-Brown, Stefan Mihalas, Eric T. Shea-Brown, 2016, NIPS.

Information theoretic approaches to understanding circuit function

Eric Shea-Brown, Andrea Barreiro, Andrea K. Barreiro, 2012, Current Opinion in Neurobiology.

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

Eric Shea-Brown, Guillaume Lajoie, Eric T. Shea-Brown, 2010, SIAM J. Appl. Dyn. Syst..

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

Eric Shea-Brown, Anitha Pasupathy, Hannah Choi, 2016, Neural Computation.

Comparison Against Task Driven Artificial Neural Networks Reveals Functional Organization of Mouse Visual Cortex

Eric Shea-Brown, Jianghong Shi, Michael A. Buice, 2019, 1911.07986.

From the statistics of connectivity to the statistics of spike times in neuronal networks

Eric Shea-Brown, Brent Doiron, Robert Rosenbaum, 2017 .

Towards blueprints for network architecture, biophysical dynamics and signal transduction

Eric Shea-Brown, Stephen Coombes, Brent Doiron, 2006, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

Add HOC?: dendritic nonlinearities shape higher-than-pairwise correlations and improve coding in noisy (spiking) neural populations

Eric Shea-Brown, Joel Zylberberg, 2013 .

Inferring Neural Population Spiking Rate from Wide-Field Calcium Imaging

Eric Shea-Brown, Daniela Witten, Merav Stern, 2020 .

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

Eric Shea-Brown, N. Alex Cayco-Gajic, Joel Zylberberg, 2018, bioRxiv.

Impact of Network Structure and Cellular Response on Spike Time Correlations

Eric Shea-Brown, Yu Hu, Kresimir Josic, 2011, PLoS Comput. Biol..

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

Eric Shea-Brown, Stefan Mihalas, R. Clay Reid, 2021, Neural Networks.

Universal properties of correlation transfer in integrate-and-fire neurons

Eric Shea-Brown, Brent Doiron, Kresimir Josic, 2007, q-bio/0703037.

Stochastic differential equation models for ion channel noise in Hodgkin-Huxley neurons.

Eric Shea-Brown, Joshua H. Goldwyn, Nikita S. Imennov, 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

of Statistics and Its Application Computational Neuroscience : Mathematical and Statistical Perspectives

Emery N. Brown, Sonja Grün, Tomoki Fukai, 2018 .

Impact of triplet correlations on neural population codes

Eric Shea-Brown, Joel Zylberberg, J. Zylberberg, 2014, 1412.0363.

A-current and type I/type II transition determine collective spiking from common input.

Eric Shea-Brown, Andrea K. Barreiro, Andrea K Barreiro, 2011, Journal of neurophysiology.

A Simple Mechanism for Beyond-Pairwise Correlations in Integrate-and-Fire Neurons

Eric Shea-Brown, David A. Leen, E. Shea-Brown, 2015, The Journal of Mathematical Neuroscience (JMN).

Motif statistics and spike correlations in neuronal networks

Eric Shea-Brown, James Trousdale, Yu Hu, 2012, BMC Neuroscience.

Reliability of Coupled Oscillators I: Two-Oscillator Systems

Eric Shea-Brown, Lai-Sang Young, Eric T. Shea-Brown, 2007, 0708.3061.

A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex

Eric Shea-Brown, Christof Koch, Fuhui Long, 2019, Nature Neuroscience.

Correlation and synchrony transfer in integrate-and-fire neurons: basic properties and consequences for coding.

Eric Shea-Brown, Brent Doiron, Kresimir Josić, 2008, Physical review letters.

Time scales of spike-train correlation for neural oscillators with common drive.

Eric Shea-Brown, Andrea K. Barreiro, Andrea K Barreiro, 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

Different roles for inhibition in the rhythm-generating respiratory network.

Eric Shea-Brown, Kameron Decker Harris, Jan-Marino Ramirez, 2016, Journal of neurophysiology.