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Correcting the hebbian mistake: Toward a fully error-driven hippocampus
The hippocampus plays a critical role in the rapid learning of new episodic memories. Many computational models propose that the hippocampus is an autoassociator that relies on Hebbian learning (i.e., “cells that fire together, wire together”). However, Hebbian learning is computationally suboptimal...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586412/ https://www.ncbi.nlm.nih.gov/pubmed/36219613 http://dx.doi.org/10.1371/journal.pcbi.1010589 |
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author | Zheng, Yicong Liu, Xiaonan L. Nishiyama, Satoru Ranganath, Charan O’Reilly, Randall C. |
author_facet | Zheng, Yicong Liu, Xiaonan L. Nishiyama, Satoru Ranganath, Charan O’Reilly, Randall C. |
author_sort | Zheng, Yicong |
collection | PubMed |
description | The hippocampus plays a critical role in the rapid learning of new episodic memories. Many computational models propose that the hippocampus is an autoassociator that relies on Hebbian learning (i.e., “cells that fire together, wire together”). However, Hebbian learning is computationally suboptimal as it does not learn in a way that is driven toward, and limited by, the objective of achieving effective retrieval. Thus, Hebbian learning results in more interference and a lower overall capacity. Our previous computational models have utilized a powerful, biologically plausible form of error-driven learning in hippocampal CA1 and entorhinal cortex (EC) (functioning as a sparse autoencoder) by contrasting local activity states at different phases in the theta cycle. Based on specific neural data and a recent abstract computational model, we propose a new model called Theremin (Total Hippocampal ERror MINimization) that extends error-driven learning to area CA3—the mnemonic heart of the hippocampal system. In the model, CA3 responds to the EC monosynaptic input prior to the EC disynaptic input through dentate gyrus (DG), giving rise to a temporal difference between these two activation states, which drives error-driven learning in the EC→CA3 and CA3↔CA3 projections. In effect, DG serves as a teacher to CA3, correcting its patterns into more pattern-separated ones, thereby reducing interference. Results showed that Theremin, compared with our original Hebbian-based model, has significantly increased capacity and learning speed. The model makes several novel predictions that can be tested in future studies. |
format | Online Article Text |
id | pubmed-9586412 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-95864122022-10-22 Correcting the hebbian mistake: Toward a fully error-driven hippocampus Zheng, Yicong Liu, Xiaonan L. Nishiyama, Satoru Ranganath, Charan O’Reilly, Randall C. PLoS Comput Biol Research Article The hippocampus plays a critical role in the rapid learning of new episodic memories. Many computational models propose that the hippocampus is an autoassociator that relies on Hebbian learning (i.e., “cells that fire together, wire together”). However, Hebbian learning is computationally suboptimal as it does not learn in a way that is driven toward, and limited by, the objective of achieving effective retrieval. Thus, Hebbian learning results in more interference and a lower overall capacity. Our previous computational models have utilized a powerful, biologically plausible form of error-driven learning in hippocampal CA1 and entorhinal cortex (EC) (functioning as a sparse autoencoder) by contrasting local activity states at different phases in the theta cycle. Based on specific neural data and a recent abstract computational model, we propose a new model called Theremin (Total Hippocampal ERror MINimization) that extends error-driven learning to area CA3—the mnemonic heart of the hippocampal system. In the model, CA3 responds to the EC monosynaptic input prior to the EC disynaptic input through dentate gyrus (DG), giving rise to a temporal difference between these two activation states, which drives error-driven learning in the EC→CA3 and CA3↔CA3 projections. In effect, DG serves as a teacher to CA3, correcting its patterns into more pattern-separated ones, thereby reducing interference. Results showed that Theremin, compared with our original Hebbian-based model, has significantly increased capacity and learning speed. The model makes several novel predictions that can be tested in future studies. Public Library of Science 2022-10-11 /pmc/articles/PMC9586412/ /pubmed/36219613 http://dx.doi.org/10.1371/journal.pcbi.1010589 Text en © 2022 Zheng et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zheng, Yicong Liu, Xiaonan L. Nishiyama, Satoru Ranganath, Charan O’Reilly, Randall C. Correcting the hebbian mistake: Toward a fully error-driven hippocampus |
title | Correcting the hebbian mistake: Toward a fully error-driven hippocampus |
title_full | Correcting the hebbian mistake: Toward a fully error-driven hippocampus |
title_fullStr | Correcting the hebbian mistake: Toward a fully error-driven hippocampus |
title_full_unstemmed | Correcting the hebbian mistake: Toward a fully error-driven hippocampus |
title_short | Correcting the hebbian mistake: Toward a fully error-driven hippocampus |
title_sort | correcting the hebbian mistake: toward a fully error-driven hippocampus |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586412/ https://www.ncbi.nlm.nih.gov/pubmed/36219613 http://dx.doi.org/10.1371/journal.pcbi.1010589 |
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