Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation

. 2022 Nov ; 18 (11) : e1010628. [epub] 20221118

Jazyk angličtina Země Spojené státy americké Médium electronic-ecollection

Typ dokumentu časopisecké články, práce podpořená grantem, Research Support, U.S. Gov't, Non-P.H.S., Research Support, N.I.H., Extramural

Perzistentní odkaz   https://www.medvik.cz/link/pmid36399437

Grantová podpora
R01 MH125557 NIMH NIH HHS - United States
R01 NS109553 NINDS NIH HHS - United States
RF1 MH117155 NIMH NIH HHS - United States

Odkazy

PubMed 36399437
PubMed Central PMC9674146
DOI 10.1371/journal.pcbi.1010628
PII: PCOMPBIOL-D-22-00638
Knihovny.cz E-zdroje

Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new training is interleaved with periods of sleep for memory consolidation. Here we used spiking network to study mechanisms behind catastrophic forgetting and the role of sleep in preventing it. The network could be trained to learn a complex foraging task but exhibited catastrophic forgetting when trained sequentially on different tasks. In synaptic weight space, new task training moved the synaptic weight configuration away from the manifold representing old task leading to forgetting. Interleaving new task training with periods of off-line reactivation, mimicking biological sleep, mitigated catastrophic forgetting by constraining the network synaptic weight state to the previously learned manifold, while allowing the weight configuration to converge towards the intersection of the manifolds representing old and new tasks. The study reveals a possible strategy of synaptic weights dynamics the brain applies during sleep to prevent forgetting and optimize learning.

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