On the Correlation between Reservoir Metrics and Performance for Time Series Classification under the Influence of Synaptic Plasticity-Reference-Cited by-同舟云学术

On the Correlation between Reservoir Metrics and Performance for Time Series Classification under the Influence of Synaptic Plasticity

Published:2014-07-10 Issue:7 Volume:9 Page:e101792
ISSN:1932-6203
Container-title:PLoS ONE
language:en
Short-container-title:PLoS ONE

Author:

Chrol-Cannon Joseph,Jin Yaochu

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference29 articles.

1. Reservoir computing approaches to recurrent neural network training;M Lukosevicius;Computer Science Review,2009

2. Reservoir computing trends;M Lukosevicius;KI - Künstliche Intelligenz,2012

3. Real-time computing without stable states: a new framework for neural computation based on perturbations;W Maass;Neural Computation,2002

4. Jaeger H (2001) The echo state approach to analysing and training recurrent neural networks. Technical Report 148, GMD-Forschungszentrum Informationstechnik.

5. A developmental approach to structural self-organization in reservoir computing;J Yin;IEEE Transactions on on Autonomous Mental Development,2012

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Liquid State Machine on Loihi: Memory Metric for Performance Prediction;Lecture Notes in Computer Science;2022

2. Neuro-Inspired Computing with Spin-VCSELs;Applied Sciences;2021-05-07

3. Effects of singular value spectrum on the performance of echo state network;Neurocomputing;2019-09

4. Spatiotemporal signal classification via principal components of reservoir states;Neural Networks;2017-07

5. Reservoir Computing as a Model for In-Materio Computing;Emergence, Complexity and Computation;2016-07-19