Learning, memory, and the role of neural network architecture.
The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both a...
Saved in:
Main Authors: | Ann M Hermundstad, Kevin S Brown, Danielle S Bassett, Jean M Carlson |
---|---|
Format: | article |
Language: | EN |
Published: |
Public Library of Science (PLoS)
2011
|
Subjects: | |
Online Access: | https://doaj.org/article/ee8ce64ee4c044f4a62b0be947a4809d |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks
by: Jannik Luboeinski, et al.
Published: (2021) -
Dendritic normalisation improves learning in sparsely connected artificial neural networks.
by: Alex D Bird, et al.
Published: (2021) -
Counterfactual choice and learning in a neural network centered on human lateral frontopolar cortex.
by: Erie D Boorman, et al.
Published: (2011) -
Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network.
by: Zhengqiao Zhao, et al.
Published: (2021) -
CAMAP: Artificial neural networks unveil the role of codon arrangement in modulating MHC-I peptides presentation.
by: Tariq Daouda, et al.
Published: (2021)