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Neural Computation|October 20, 2020
Passive Nonlinear Dendritic Interactions as a Computational Resource in Spiking Neural NetworksAndreas Stöckel, Chris EliasmithTopics in Cognitive Science|June 19, 2021
Connecting Biological Detail With Neural Computation: Application to the Cerebellar Granule-Golgi MicrocircuitAndreas Stöckel, Terrence C Stewart, Chris EliasmithBrain Sciences|February 25, 2023
Biologically-Based Computation: How Neural Details and Dynamics Are Suited for Implementing a Variety of AlgorithmsNicole Sandra-Yaffa Dumont, Andreas Stöckel, P Michael Furlong, et al.Studies in History and Philosophy of Science|April 7, 2011
How we ought to describe computation in the brainChris EliasmithTopics in Cognitive Science|January 19, 2012
The complex systems approach: rhetoric or revolutionChris EliasmithNeural Computation|May 20, 2005
A unified approach to building and controlling spiking attractor networksChris EliasmithPlos One|February 23, 2016
Optimizing Semantic Pointer Representations for Symbol-Like Processing in Spiking Neural NetworksJan Gosmann, Chris EliasmithBiological Cybernetics|May 17, 2011
Normalization for probabilistic inference with neuronsChris Eliasmith, James MartensCerebral Cortex (New York, N.Y. : 1991)|October 18, 2006
Neural populations can induce reliable postsynaptic currents without observable spike rate changes or precise spike timingBryan Tripp, Chris EliasmithThe Behavioral and Brain Sciences|May 14, 2013
God, the devil, and the details: Fleshing out the predictive processing frameworkDaniel Rasmussen, Chris EliasmithPageof 6