Eric Shea‐Brown

4.7k total citations
76 papers, 2.4k citations indexed

About

Eric Shea‐Brown is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Statistical and Nonlinear Physics. According to data from OpenAlex, Eric Shea‐Brown has authored 76 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 67 papers in Cognitive Neuroscience, 31 papers in Cellular and Molecular Neuroscience and 27 papers in Statistical and Nonlinear Physics. Recurrent topics in Eric Shea‐Brown's work include Neural dynamics and brain function (65 papers), stochastic dynamics and bifurcation (25 papers) and Neuroscience and Neural Engineering (19 papers). Eric Shea‐Brown is often cited by papers focused on Neural dynamics and brain function (65 papers), stochastic dynamics and bifurcation (25 papers) and Neuroscience and Neural Engineering (19 papers). Eric Shea‐Brown collaborates with scholars based in United States, United Kingdom and Canada. Eric Shea‐Brown's co-authors include Krešimir Josić́, Brent Doiron, Jaime de la Rocha, Alex D. Reyes, Joshua H. Goldwyn, Herschel Rabitz, Yu Hu, Joel Zylberberg, Jeff Moehlis and Xiao‐Jiang Feng and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Physical Review Letters.

In The Last Decade

Eric Shea‐Brown

74 papers receiving 2.4k citations

Peers

Countries citing papers authored by Eric Shea‐Brown

Since Specialization

Citations

This map shows the geographic impact of Eric Shea‐Brown's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Eric Shea‐Brown with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric Shea‐Brown more than expected).

Fields of papers citing papers by Eric Shea‐Brown

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eric Shea‐Brown. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Eric Shea‐Brown. The network helps show where Eric Shea‐Brown may publish in the future.

Co-authorship network of co-authors of Eric Shea‐Brown

This figure shows the co-authorship network connecting the top 25 collaborators of Eric Shea‐Brown. A scholar is included among the top collaborators of Eric Shea‐Brown based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Eric Shea‐Brown. Eric Shea‐Brown is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Impact of Local Connectivity Patterns on Excitatory-Inhibitory Network Dynamics 2025 ·SPIRE - Sciences Po Institutional REpository ·(unknown), David Dahmen, Stefano Recanatesi, Eric Shea‐Brown, Srdjan Ostojic	1
2	A biologically inspired architecture with switching units can learn to generalize across backgrounds 2023 ·Neural Networks ·(unknown), Eric Shea‐Brown, Ştefan Mihalaş	1
3	MouseNet: A biologically constrained convolutional neural network model for the mouse visual cortex 2022 ·PLoS Computational Biology ·Jianghong Shi, (unknown), Eric Shea‐Brown, Ştefan Mihalaş, Michael A. Buice	11
4	Nonlinear convergence boosts information coding in circuits with parallel outputs 2021 ·Proceedings of the National Academy of Sciences ·Gabrielle J. Gutierrez, Fred Rieke, Eric Shea‐Brown	4
5	Cell-type–specific neuromodulation guides synaptic credit assignment in a spiking neural network 2021 ·Proceedings of the National Academy of Sciences ·(unknown), Stephen J Smith, Ştefan Mihalaş, Eric Shea‐Brown, Uygar Sümbül	14
6	Predictive learning as a network mechanism for extracting low-dimensional latent space representations 2021 ·Nature Communications ·Stefano Recanatesi, (unknown), Guillaume Lajoie, Sophie Denève, Mattia Rigotti, Eric Shea‐Brown	40
7	Autoencoder networks extract latent variables and encode these variables in their connectomes 2021 ·Neural Networks ·(unknown), Stefano Recanatesi, R. Clay Reid, Ştefan Mihalaş, Eric Shea‐Brown	6
8	Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex 2019 ·arXiv (Cornell University) ·Jianghong Shi, Eric Shea‐Brown, Michael A. Buice	4
9	Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity 2019 ·PLoS Computational Biology ·Stefano Recanatesi, Gabriel Koch Ocker, Michael A. Buice, Eric Shea‐Brown	42
10	High-resolution data-driven model of the mouse connectome 2018 ·Network Neuroscience ·Joseph E. Knox, Kameron Decker Harris, Nile Graddis, Jennifer D. Whitesell, Hongkui Zeng, Julie A. Harris, Eric Shea‐Brown, Ştefan Mihalaş	58
11	Encoding in Balanced Networks: Revisiting Spike Patterns and Chaos in Stimulus-Driven Systems 2016 ·PLoS Computational Biology ·Guillaume Lajoie, (unknown), (unknown), Eric Shea‐Brown	12
12	Nonnegative spline regression of incomplete tracing data reveals high resolution neural connectivity 2016 ·arXiv (Cornell University) ·Kameron Decker Harris, Ştefan Mihalaş, Eric Shea‐Brown	1
13	Triplet correlations among similarly tuned cells impact population coding 2015 ·Frontiers in Computational Neuroscience ·N. Alex Cayco-Gajic, Joel Zylberberg, Eric Shea‐Brown	17
14	The sign rule and beyond: Boundary effects, flexibility, and optimal noise correlations in neural population codes 2013 ·arXiv (Cornell University) ·(unknown), Joel Zylberberg, Eric Shea‐Brown	3
15	Impact of Network Structure and Cellular Response on Spike Time Correlations 2012 ·PLoS Computational Biology ·(unknown), Yu Hu, Eric Shea‐Brown, Krešimir Josić́	111
16	Computational models of decision making: integration, stability, and noise 2012 ·Current Opinion in Neurobiology ·Nicholas Cain, Eric Shea‐Brown	13
17	When are microcircuits well-modeled by maximum entropy methods? 2010 ·SHILAP Revista de lepidopterología ·(unknown), Eric Shea‐Brown, (unknown), Julijana Gjorgjieva	1
18	Encoding and decoding amplitude-modulated cochlear implant stimuli—a point process analysis 2010 ·Journal of Computational Neuroscience ·Joshua H. Goldwyn, Eric Shea‐Brown, Jay T. Rubinstein	20
19	Correlation between neural spike trains increases with firing rate 2007 ·Nature ·Jaime de la Rocha, Brent Doiron, Eric Shea‐Brown, Krešimir Josić́, Alex D. Reyes	491
20	Optimal deep brain stimulation of the subthalamic nucleus—a computational study 2007 ·Journal of Computational Neuroscience ·Xiao‐Jiang Feng, Eric Shea‐Brown, Brian D. Greenwald, Robert L. Kosut, Herschel Rabitz	78

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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