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...

Full description

Bibliographic Details
Main Authors: Nimrod Shaham, Jay Chandra, Gabriel Kreiman, Haim Sompolinsky
Format: Article
Language:English
Published: Nature Portfolio 2022-07-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-022-16407-9
_version_ 1811222216157691904
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