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Bryan P Tripp
Chris Eliasmith

Neural computation

Showing results (1-10 of 10) with videos related to

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Neural Computation|November 20, 2009
Population models of temporal differentiationBryan P Tripp, Chris Eliasmith
Neural Computation|May 20, 2005
A unified approach to building and controlling spiking attractor networksChris Eliasmith
Neural Computation|October 20, 2020
Passive Nonlinear Dendritic Interactions as a Computational Resource in Spiking Neural NetworksAndreas Stöckel, Chris Eliasmith
Neural Computation|March 17, 2015
Surrogate population models for large-scale neural simulationsBryan P Tripp
Neural Computation|December 16, 2011
Decorrelation of spiking variability and improved information transfer through feedforward divisive normalizationBryan P Tripp
Neural Computation|March 19, 2019
Vector-Derived Transformation Binding: An Improved Binding Operation for Deep Symbol-Like Processing in Neural NetworksJan Gosmann, Chris Eliasmith
Neural Computation|December 9, 2017
Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time CellsAaron R Voelker, Chris Eliasmith
Neural Computation|June 1, 2014
The competing benefits of noise and heterogeneity in neural codingEric Hunsberger, Matthew Scott, Chris Eliasmith
Neural Computation|February 8, 2008
Solving the problem of negative synaptic weights in cortical modelsChristopher Parisien, Charles H Anderson, Chris Eliasmith
Neural Computation|July 26, 2021
Simulating and Predicting Dynamical Systems With Spatial Semantic PointersAaron R Voelker, Peter Blouw, Xuan Choo, et al.
Pageof 1

Showing results (1-10 of 10) with videos related to

Sort By:
Pageof 1
Neural Computation|November 20, 2009
Population models of temporal differentiationBryan P Tripp, Chris Eliasmith
Neural Computation|May 20, 2005
A unified approach to building and controlling spiking attractor networksChris Eliasmith
Neural Computation|October 20, 2020
Passive Nonlinear Dendritic Interactions as a Computational Resource in Spiking Neural NetworksAndreas Stöckel, Chris Eliasmith
Neural Computation|March 17, 2015
Surrogate population models for large-scale neural simulationsBryan P Tripp
Neural Computation|December 16, 2011
Decorrelation of spiking variability and improved information transfer through feedforward divisive normalizationBryan P Tripp
Neural Computation|March 19, 2019
Vector-Derived Transformation Binding: An Improved Binding Operation for Deep Symbol-Like Processing in Neural NetworksJan Gosmann, Chris Eliasmith
Neural Computation|December 9, 2017
Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time CellsAaron R Voelker, Chris Eliasmith
Neural Computation|June 1, 2014
The competing benefits of noise and heterogeneity in neural codingEric Hunsberger, Matthew Scott, Chris Eliasmith
Neural Computation|February 8, 2008
Solving the problem of negative synaptic weights in cortical modelsChristopher Parisien, Charles H Anderson, Chris Eliasmith
Neural Computation|July 26, 2021
Simulating and Predicting Dynamical Systems With Spatial Semantic PointersAaron R Voelker, Peter Blouw, Xuan Choo, et al.
Pageof 1