Biologically plausible learning in neural networks: a lesson from bacterial chemotaxis

DOI10.1007/S00422-009-0341-6zbMath1344.92023OpenAlexW1991270096WikidataQ48428793 ScholiaQ48428793MaRDI QIDQ309492

Yury P. Shimansky

Publication date: 7 September 2016

Published in: Biological Cybernetics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00422-009-0341-6

zbMATH Keywords

neural networkslearningbacterial chemotaxisbiological optimizationmodification probability

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Cell movement (chemotaxis, etc.) (92C17)

Related Items (1)

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Cites Work

• Optimization by Simulated Annealing
• A novel model of motor learning capable of developing an optimal movement control law online from scratch

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