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Eric Shea-Brown

Showing results (21-30 of 66) with videos related to

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Frontiers in Computational Neuroscience|August 3, 2013
A generative spike train model with time-structured higher order correlationsJames Trousdale, Yu Hu, Eric Shea-Brown, et al.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|April 7, 2010
Time scales of spike-train correlation for neural oscillators with common driveAndrea K Barreiro, Eric Shea-Brown, Evan L Thilo
Journal of Neurophysiology|June 8, 2012
A-current and type I/type II transition determine collective spiking from common inputAndrea K Barreiro, Evan L Thilo, Eric Shea-Brown
Plos Computational Biology|March 30, 2012
Impact of network structure and cellular response on spike time correlationsJames Trousdale, Yu Hu, Eric Shea-Brown, et al.
Entropy (Basel, Switzerland)|December 3, 2020
A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural DataN Alex Cayco-Gajic, Joel Zylberberg, Eric Shea-Brown
Frontiers in Computational Neuroscience|June 5, 2015
Triplet correlations among similarly tuned cells impact population codingNatasha A Cayco-Gajic, Joel Zylberberg, Eric Shea-Brown
Neural Computation|July 16, 2008
Optimization of decision making in multilayer networks: the role of locus coeruleusEric Shea-Brown, Mark S Gilzenrat, Jonathan D Cohen
Journal of Computational Neuroscience|January 22, 2009
Spike-time reliability of layered neural oscillator networksKevin K Lin, Eric Shea-Brown, Lai-Sang Young
Journal of Neurophysiology|March 1, 2013
Neural integrators for decision making: a favorable tradeoff between robustness and sensitivityNicholas Cain, Andrea K Barreiro, Michael Shadlen, et al.
Brain Research|January 18, 2006
A firing rate model of Parkinsonian deficits in interval timingEric Shea-Brown, John Rinzel, Brian C Rakitin, et al.
Pageof 7

Showing results (21-30 of 66) with videos related to

Sort By:
Pageof 7
Frontiers in Computational Neuroscience|August 3, 2013
A generative spike train model with time-structured higher order correlationsJames Trousdale, Yu Hu, Eric Shea-Brown, et al.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|April 7, 2010
Time scales of spike-train correlation for neural oscillators with common driveAndrea K Barreiro, Eric Shea-Brown, Evan L Thilo
Journal of Neurophysiology|June 8, 2012
A-current and type I/type II transition determine collective spiking from common inputAndrea K Barreiro, Evan L Thilo, Eric Shea-Brown
Plos Computational Biology|March 30, 2012
Impact of network structure and cellular response on spike time correlationsJames Trousdale, Yu Hu, Eric Shea-Brown, et al.
Entropy (Basel, Switzerland)|December 3, 2020
A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural DataN Alex Cayco-Gajic, Joel Zylberberg, Eric Shea-Brown
Frontiers in Computational Neuroscience|June 5, 2015
Triplet correlations among similarly tuned cells impact population codingNatasha A Cayco-Gajic, Joel Zylberberg, Eric Shea-Brown
Neural Computation|July 16, 2008
Optimization of decision making in multilayer networks: the role of locus coeruleusEric Shea-Brown, Mark S Gilzenrat, Jonathan D Cohen
Journal of Computational Neuroscience|January 22, 2009
Spike-time reliability of layered neural oscillator networksKevin K Lin, Eric Shea-Brown, Lai-Sang Young
Journal of Neurophysiology|March 1, 2013
Neural integrators for decision making: a favorable tradeoff between robustness and sensitivityNicholas Cain, Andrea K Barreiro, Michael Shadlen, et al.
Brain Research|January 18, 2006
A firing rate model of Parkinsonian deficits in interval timingEric Shea-Brown, John Rinzel, Brian C Rakitin, et al.
Pageof 7