Role of NMDAR plasticity in a computational model of synaptic memory.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
27 10 2021
27 10 2021
Historique:
received:
30
01
2021
accepted:
12
10
2021
entrez:
28
10
2021
pubmed:
29
10
2021
medline:
27
1
2022
Statut:
epublish
Résumé
A largely unexplored question in neuronal plasticity is whether synapses are capable of encoding and learning the timing of synaptic inputs. We address this question in a computational model of synaptic input time difference learning (SITDL), where N-methyl-d-aspartate receptor (NMDAR) isoform expression in silent synapses is affected by time differences between glutamate and voltage signals. We suggest that differences between NMDARs' glutamate and voltage gate conductances induce modifications of the synapse's NMDAR isoform population, consequently changing the timing of synaptic response. NMDAR expression at individual synapses can encode the precise time difference between signals. Thus, SITDL enables the learning and reconstruction of signals across multiple synapses of a single neuron. In addition to plausibly predicting the roles of NMDARs in synaptic plasticity, SITDL can be usefully applied in artificial neural network models.
Identifiants
pubmed: 34707139
doi: 10.1038/s41598-021-00516-y
pii: 10.1038/s41598-021-00516-y
pmc: PMC8551337
doi:
Substances chimiques
Protein Isoforms
0
Receptors, N-Methyl-D-Aspartate
0
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
21182Informations de copyright
© 2021. The Author(s).
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