A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity | Citedby Results | Neural Computation | MIT Press

Skip to Main Content

Skip Nav Destination

A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity

Neural Computation (2019) 31 (7): 1430–1461.

This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.

The onset of motor learning impairments in Parkinson’s disease: a computational investigation

Ilaria Gigi
Rosa Senatore
Angelo Marcelli

Brain Informatics (2024) 11 (1)

Learning Fixed Points of Recurrent Neural Networks by Reparameterizing the Network Model

Vicky Zhu
Robert Rosenbaum

Neural Computation (2024) 36 (8): 1568.

Secure Communication via Chaotic Synchronization Based on Reservoir Computing

Jiayue Liu
Jianguo Zhang
Yuncai Wang

IEEE Transactions on Neural Networks and Learning Systems (2024) 35 (1): 285.

Weight versus Node Perturbation Learning in Temporally Extended Tasks: Weight Perturbation Often Performs Similarly or Better

Paul Züge
Christian Klos
Raoul-Martin Memmesheimer

Physical Review X (2023) 13 (2)

Spatiotemporal motor learning with reward-modulated Hebbian plasticity in modular reservoir computing

Yuji Kawai
Minoru Asada

Neurocomputing (2023) 558: 126740.

Hebbian plasticity in parallel synaptic pathways: A circuit mechanism for systems memory consolidation

Michiel W. H. Remme
Urs Bergmann
Denis Alevi
Susanne Schreiber
Henning Sprekeler
Richard Kempter
Daniel Bush

PLOS Computational Biology (2021) 17 (12): e1009681.

Remembrance of things practiced with fast and slow learning in cortical and subcortical pathways

James M. Murray
G. Sean Escola

Nature Communications (2020) 11 (1)