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...
Guardado en:
Autores principales: | Jannik Luboeinski, Christian Tetzlaff |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/b13fad3d6a5d49d8a558acdd08c9cfb0 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
State based model of long-term potentiation and synaptic tagging and capture.
por: Adam B Barrett, et al.
Publicado: (2009) -
Decorrelation of neural-network activity by inhibitory feedback.
por: Tom Tetzlaff, et al.
Publicado: (2012) -
Synaptic plasticity in neural networks needs homeostasis with a fast rate detector.
por: Friedemann Zenke, et al.
Publicado: (2013) -
Learning, memory, and the role of neural network architecture.
por: Ann M Hermundstad, et al.
Publicado: (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.
por: Quinton M Skilling, et al.
Publicado: (2021)