Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity-Reference-Cited by-同舟云学术

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Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity

Published:2024-01-25 Issue: Volume: Page:
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Armendarez Nicholas X.¹,Mohamed Ahmed S.¹,Dhungel Anurag²,Hossain Md Razuan²,Hasan Md Sakib²^ORCID,Najem Joseph S.¹^ORCID

Affiliation:

1. Department of Mechanical Engineering, The Pennsylvania State University, 336 Reber Building, University Park, Pennsylvania 16802, United States

2. Department of Electrical and Computer Engineering, The University of Mississippi, 310 Anderson Hall, University, Mississippi 38677, United States

Publisher

American Chemical Society (ACS)

Subject

General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.3c16003

Reference43 articles.

1. Jaeger, H. The “Echo State” Approach to Analysing and Training Recurrent Neural Networks-With an Erratum Note, German National Research Center for Information Technology GMD Technical Report: Bonn, Germany, 2001; Vol. 148, p 13.

2. Jaeger, H. Adaptive Nonlinear System Identification With Echo State Networks. Advances in Neural Information Processing Systems 15, 2002; pp 609–616.

3. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations

4. Information processing using a single dynamical node as complex system

5. Rotating neurons for all-analog implementation of cyclic reservoir computing

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