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Living machinesA handbook of research in biomimetics and biohybrid systems$
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Tony J. Prescott, Nathan Lepora, and Paul F.M.J Verschure

Print publication date: 2018

Print ISBN-13: 9780199674923

Published to Oxford Scholarship Online: June 2018

DOI: 10.1093/oso/9780199674923.001.0001

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Learning and control

Learning and control

Chapter:
(p.239) Chapter 26 Learning and control
Source:
Living machines
Author(s):

Ivan Herreros

Publisher:
Oxford University Press
DOI:10.1093/oso/9780199674923.003.0026

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

Keywords:   feedback control, feed-forward control, supervised learning, unsupervised learning, reinforcement, learning, classical conditioning, operant conditioning, reflex, anticipatory reflex

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