Storing structured sparse memories in a multi-modular cortical network model.

Journal Article (Journal Article)

We study the memory performance of a class of modular attractor neural networks, where modules are potentially fully-connected networks connected to each other via diluted long-range connections. On this anatomical architecture we store memory patterns of activity using a Willshaw-type learning rule. P patterns are split in categories, such that patterns of the same category activate the same set of modules. We first compute the maximal storage capacity of these networks. We then investigate their error-correction properties through an exhaustive exploration of parameter space, and identify regions where the networks behave as an associative memory device. The crucial parameters that control the retrieval abilities of the network are (1) the ratio between the number of synaptic contacts of long- and short-range origins (2) the number of categories in which a module is activated and (3) the amount of local inhibition. We discuss the relationship between our model and networks of cortical patches that have been observed in different cortical areas.

Full Text

Duke Authors

Cited Authors

  • Dubreuil, AM; Brunel, N

Published Date

  • April 2016

Published In

Volume / Issue

  • 40 / 2

Start / End Page

  • 157 - 175

PubMed ID

  • 26852335

Electronic International Standard Serial Number (EISSN)

  • 1573-6873

Digital Object Identifier (DOI)

  • 10.1007/s10827-016-0590-z


  • eng

Conference Location

  • United States