Attractor dynamics in a modular network model of the cerebral cortex

Authors:
Mikael Lundqvist;Martin Rehn;Anders Lansner
Affiliations:
Department of Numerical Analysis and Computer Science (NADA), Royal Institute of Technology, Sweden;Department of Numerical Analysis and Computer Science (NADA), Royal Institute of Technology, Sweden;Stockholm University, Stockholm, Sweden
Venue:
Neurocomputing
Year:
2006

Citing 2
Cited 2

Modeling brain function—the world of attractor neural networks

Modeling brain function—the world of attractor neural networks
Introduction to the theory of neural computation

Introduction to the theory of neural computation

Imposing Biological Constraints onto an Abstract Neocortical Attractor Network Model

Neural Computation
A New Associative Model with Dynamical Synapses

Neural Processing Letters

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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.