Learning by mistakes in memristor networks
Author:
Funder
Consejo Nacional de Investigaciones Científicas y Técnicas
Publisher
American Physical Society (APS)
Link
http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevE.105.054306/fulltext
Reference23 articles.
1. Physics for neuromorphic computing
2. Learning from mistakes
3. Adaptive learning by extremal dynamics and negative feedback
4. Order–disorder transition in the Chialvo–Bak ‘minibrain’ controlled by network geometry
5. Adaptivity and ‘Per learning’
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1. A Novel Memristors Based Echo State Network Model Inspired by the Brain’s Uni-hemispheric Slow-Wave Sleep Characteristics;Cognitive Computation;2024-06-10
2. Cycle equivalence classes, orthogonal Weingarten calculus, and the mean field theory of memristive systems;Neuromorphic Computing and Engineering;2024-05-09
3. Blooming and pruning: learning from mistakes with memristive synapses;Scientific Reports;2024-04-02
4. Ergodicity, lack thereof, and the performance of reservoir computing with memristive networks;Nano Express;2024-03-01
5. AC power analysis for second-order memory elements;Frontiers in Physics;2023-02-17
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