Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks
Luboeinski and Tetzlaff develop a theoretical model, which integrates mechanisms underlying the synaptic-tagging-and-capture (STC) hypothesis with calcium-based synaptic plasticity in a recurrent spiking neural network, to describe consolidation of memory representations. They show that STC mechanis...
Enregistré dans:
Auteurs principaux: | Jannik Luboeinski, Christian Tetzlaff |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/b13fad3d6a5d49d8a558acdd08c9cfb0 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
State based model of long-term potentiation and synaptic tagging and capture.
par: Adam B Barrett, et autres
Publié: (2009) -
Decorrelation of neural-network activity by inhibitory feedback.
par: Tom Tetzlaff, et autres
Publié: (2012) -
Synaptic plasticity in neural networks needs homeostasis with a fast rate detector.
par: Friedemann Zenke, et autres
Publié: (2013) -
Learning, memory, and the role of neural network architecture.
par: Ann M Hermundstad, et autres
Publié: (2011) -
Acetylcholine-gated current translates wake neuronal firing rate information into a spike timing-based code in Non-REM sleep, stabilizing neural network dynamics during memory consolidation.
par: Quinton M Skilling, et autres
Publié: (2021)