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

dc.contributor.author

Dubreuil, Alexis M

dc.contributor.author

Brunel, Nicolas

dc.date.accessioned

2021-06-06T16:11:32Z

dc.date.available

2021-06-06T16:11:32Z

dc.date.issued

2016-04

dc.date.updated

2021-06-06T16:11:31Z

dc.description.abstract

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.

dc.identifier

10.1007/s10827-016-0590-z

dc.identifier.issn

0929-5313

dc.identifier.issn

1573-6873

dc.identifier.uri

https://hdl.handle.net/10161/23354

dc.language

eng

dc.publisher

Springer Science and Business Media LLC

dc.relation.ispartof

Journal of computational neuroscience

dc.relation.isversionof

10.1007/s10827-016-0590-z

dc.subject

Cerebral Cortex

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Nerve Net

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Neurons

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Animals

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Humans

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Memory

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Nonlinear Dynamics

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Models, Neurological

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Computer Simulation

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Neural Networks, Computer

dc.title

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

dc.type

Journal article

pubs.begin-page

157

pubs.end-page

175

pubs.issue

2

pubs.organisational-group

School of Medicine

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Physics

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Neurobiology

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Duke Institute for Brain Sciences

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Center for Cognitive Neuroscience

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Duke

pubs.organisational-group

Trinity College of Arts & Sciences

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Basic Science Departments

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University Institutes and Centers

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Institutes and Provost's Academic Units

pubs.publication-status

Published

pubs.volume

40

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