Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons

Huyck, Christian R. ORCID: https://orcid.org/0000-0003-4015-3549 and Vergani, Alberto Arturo (2020) Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons. Journal of Computational Neuroscience, 48 (3) . pp. 299-316. ISSN 0929-5313 [Article] (doi:10.1007/s10827-020-00758-1)

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Abstract

Networks of spiking neurons can have persistently firing stable bump attractors to represent continuous spaces (like temperature). This can be done with a topology with local excitatory synapses and local surround inhibitory synapses. Activating large ranges in the attractor can lead to multiple bumps, that show repeller and attractor dynamics; however, these bumps can be merged by overcoming the repeller dynamics. A simple associative memory can include these bump attractors, allowing the use of continuous variables in these memories, and these associations can be learned by Hebbian rules. These simulations are related to biological networks, showing that this is a step toward a more complete neural cognitive associative memory.

Item Type: Article
Keywords (uncontrolled): Associative memory, Bump attractor, Cell assemblies, Hebbian learning., Spiking neurons
Research Areas: A. > School of Science and Technology > Computer Science > Artificial Intelligence group
Item ID: 30653
Notes on copyright: This is a post-peer-review, pre-copyedit version of an article published in Journal of Computational Neuroscience. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10827-020-00758-1
Useful Links:
Depositing User: Chris Huyck
Date Deposited: 13 Jul 2020 16:45
Last Modified: 27 Jul 2021 03:04
URI: https://eprints.mdx.ac.uk/id/eprint/30653

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