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H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks

View ORCID ProfileThomas Limbacher, View ORCID ProfileRobert Legenstein
doi: https://doi.org/10.1101/2020.07.01.180372
Thomas Limbacher
1Institute of Theoretical Computer Science, Graz University of Technology, 8010 Graz, Austria
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Robert Legenstein
1Institute of Theoretical Computer Science, Graz University of Technology, 8010 Graz, Austria
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  • For correspondence: robert.legenstein@igi.tugraz.at
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Abstract

The ability to base current computations on memories from the past is critical for many cognitive tasks such as story understanding. Hebbian-type synaptic plasticity is believed to underlie the retention of memories over medium and long time scales in the brain. However, it is unclear how such plasticity processes are integrated with computations in cortical networks. Here, we propose Hebbian Memory Networks (H-Mems), a simple neural network model that is built around a core hetero-associative network subject to Hebbian plasticity. We show that the network can be optimized to utilize the Hebbian plasticity processes for its computations. H-Mems can one-shot memorize associations between stimulus pairs and use these associations for decisions later on. Furthermore, they can solve demanding question-answering tasks on synthetic stories. Our study shows that neural network models are able to enrich their computations with memories through simple Hebbian plasticity processes.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • thomas.limbacher{at}igi.tugraz.at

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted July 01, 2020.
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H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks
Thomas Limbacher, Robert Legenstein
bioRxiv 2020.07.01.180372; doi: https://doi.org/10.1101/2020.07.01.180372
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H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks
Thomas Limbacher, Robert Legenstein
bioRxiv 2020.07.01.180372; doi: https://doi.org/10.1101/2020.07.01.180372

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