Stochastic consolidation of lifelong memory
Abstract Humans have the remarkable ability to continually store new memories, while maintaining old memories for a lifetime. How the brain avoids catastrophic forgetting of memories due to interference between encoded memories is an open problem in computational neuroscience. Here we present a mode...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
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Nature Portfolio
2022-07-01
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Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-022-16407-9 |
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author | Nimrod Shaham Jay Chandra Gabriel Kreiman Haim Sompolinsky |
author_facet | Nimrod Shaham Jay Chandra Gabriel Kreiman Haim Sompolinsky |
author_sort | Nimrod Shaham |
collection | DOAJ |
description | Abstract Humans have the remarkable ability to continually store new memories, while maintaining old memories for a lifetime. How the brain avoids catastrophic forgetting of memories due to interference between encoded memories is an open problem in computational neuroscience. Here we present a model for continual learning in a recurrent neural network combining Hebbian learning, synaptic decay and a novel memory consolidation mechanism: memories undergo stochastic rehearsals with rates proportional to the memory’s basin of attraction, causing self-amplified consolidation. This mechanism gives rise to memory lifetimes that extend much longer than the synaptic decay time, and retrieval probability of memories that gracefully decays with their age. The number of retrievable memories is proportional to a power of the number of neurons. Perturbations to the circuit model cause temporally-graded retrograde and anterograde deficits, mimicking observed memory impairments following neurological trauma. |
first_indexed | 2024-04-12T08:12:13Z |
format | Article |
id | doaj.art-54dedfb80e334a1f949ae7e8ce9fd889 |
institution | Directory Open Access Journal |
issn | 2045-2322 |
language | English |
last_indexed | 2024-04-12T08:12:13Z |
publishDate | 2022-07-01 |
publisher | Nature Portfolio |
record_format | Article |
series | Scientific Reports |
spelling | doaj.art-54dedfb80e334a1f949ae7e8ce9fd8892022-12-22T03:40:56ZengNature PortfolioScientific Reports2045-23222022-07-0112111810.1038/s41598-022-16407-9Stochastic consolidation of lifelong memoryNimrod Shaham0Jay Chandra1Gabriel Kreiman2Haim Sompolinsky3Center for Brain Science, Harvard UniversityCenter for Brain Science, Harvard UniversityHarvard Medical SchoolCenter for Brain Science, Harvard UniversityAbstract Humans have the remarkable ability to continually store new memories, while maintaining old memories for a lifetime. How the brain avoids catastrophic forgetting of memories due to interference between encoded memories is an open problem in computational neuroscience. Here we present a model for continual learning in a recurrent neural network combining Hebbian learning, synaptic decay and a novel memory consolidation mechanism: memories undergo stochastic rehearsals with rates proportional to the memory’s basin of attraction, causing self-amplified consolidation. This mechanism gives rise to memory lifetimes that extend much longer than the synaptic decay time, and retrieval probability of memories that gracefully decays with their age. The number of retrievable memories is proportional to a power of the number of neurons. Perturbations to the circuit model cause temporally-graded retrograde and anterograde deficits, mimicking observed memory impairments following neurological trauma.https://doi.org/10.1038/s41598-022-16407-9 |
spellingShingle | Nimrod Shaham Jay Chandra Gabriel Kreiman Haim Sompolinsky Stochastic consolidation of lifelong memory Scientific Reports |
title | Stochastic consolidation of lifelong memory |
title_full | Stochastic consolidation of lifelong memory |
title_fullStr | Stochastic consolidation of lifelong memory |
title_full_unstemmed | Stochastic consolidation of lifelong memory |
title_short | Stochastic consolidation of lifelong memory |
title_sort | stochastic consolidation of lifelong memory |
url | https://doi.org/10.1038/s41598-022-16407-9 |
work_keys_str_mv | AT nimrodshaham stochasticconsolidationoflifelongmemory AT jaychandra stochasticconsolidationoflifelongmemory AT gabrielkreiman stochasticconsolidationoflifelongmemory AT haimsompolinsky stochasticconsolidationoflifelongmemory |