Attractor neural networks with patchy connectivity

Authors:
Christopher Johansson;Martin Rehn;Anders Lansner
Affiliations:
Royal Institute of Technology, Department of Numerical Analysis and Computer Science, 100 44 Stockholm, Sweden;Royal Institute of Technology, Department of Numerical Analysis and Computer Science, 100 44 Stockholm, Sweden;Royal Institute of Technology, Department of Numerical Analysis and Computer Science, 100 44 Stockholm, Sweden
Venue:
Neurocomputing
Year:
2006

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Abstract

The neurons in the mammalian visual cortex are arranged in columnar structures, and the synaptic contacts of the pyramidal neurons in layer II/III are clustered into patches that are sparsely distributed over the surrounding cortical surface. Here, we use an attractor neural-network model of the cortical circuitry and investigate the effects of patchy connectivity, both on the properties of the network and the attractor dynamics. An analysis of the network shows that the signal-to-noise ratio of the synaptic potential sums are improved by the patchy connectivity, which results in a higher storage capacity. This analysis is performed for both the Hopfield and Willshaw learning rules and the results are confirmed by simulation experiments.