Transferring learning from external to internal weights in echo-state networks with sparse connectivity.
Modifying weights within a recurrent network to improve performance on a task has proven to be difficult. Echo-state networks in which modification is restricted to the weights of connections onto network outputs provide an easier alternative, but at the expense of modifying the typically sparse arc...
Main Authors: | David Sussillo, L F Abbott |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2012-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC3360031?pdf=render |
Similar Items
-
Internal Models as Echo State Networks : Learning to Execute Arm Movements
by: Løvlid, Rikke Amilde
Published: (2013) -
Tailoring Echo State Networks for Optimal Learning
by: Pau Vilimelis Aceituno, et al.
Published: (2020-09-01) -
Resting state BOLD functional connectivity at 3T: spin echo versus gradient echo EPI.
by: Piero Chiacchiaretta, et al.
Published: (2015-01-01) -
Hippocampus as an Echo State Network
by: Mutalik, Prabhanjan
Published: (2018) -
Mixed Norm Constrained Sparse APA Algorithm for Satellite and Network Echo Channel Estimation
by: Yingsong Li, et al.
Published: (2018-01-01)