Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
408730 | Neurocomputing | 2006 | 5 Pages |
Abstract
Computational models of cortical associative memory often take a top-down approach. We have previously described such an abstract model with a hypercolumnar structure. Here we explore a similar, biophysically detailed but subsampled network model of neocortex. We study how the neurodynamics and associative memory properties of this biophysical model relate to the abstract model as well as to experimental data. The resulting network exhibits attractor dynamics; pattern completion and pattern rivalry. It reproduces several features of experimentally observed local UP states, as well as oscillatory behavior on the gamma and theta time scales observed in the cerebral cortex.
Keywords
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Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Mikael Lundqvist, Martin Rehn, Anders Lansner,