Fast social-like learning of complex behaviors based on motor motifs

Carlos Calvo Tapia, Ivan Y. Tyukin, and Valeri A. Makarov
Phys. Rev. E 97, 052308 – Published 24 May 2018

Abstract

Social learning is widely observed in many species. Less experienced agents copy successful behaviors exhibited by more experienced individuals. Nevertheless, the dynamical mechanisms behind this process remain largely unknown. Here we assume that a complex behavior can be decomposed into a sequence of n motor motifs. Then a neural network capable of activating motor motifs in a given sequence can drive an agent. To account for (n1)! possible sequences of motifs in a neural network, we employ the winnerless competition approach. We then consider a teacher-learner situation: one agent exhibits a complex movement, while another one aims at mimicking the teacher's behavior. Despite the huge variety of possible motif sequences we show that the learner, equipped with the provided learning model, can rewire “on the fly” its synaptic couplings in no more than (n1) learning cycles and converge exponentially to the durations of the teacher's motifs. We validate the learning model on mobile robots. Experimental results show that the learner is indeed capable of copying the teacher's behavior composed of six motor motifs in a few learning cycles. The reported mechanism of learning is general and can be used for replicating different functions, including, for example, sound patterns or speech.

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  • Received 22 January 2018
  • Revised 10 April 2018

DOI:https://doi.org/10.1103/PhysRevE.97.052308

©2018 American Physical Society

Physics Subject Headings (PhySH)

Nonlinear DynamicsInterdisciplinary PhysicsNetworksPhysics of Living Systems

Authors & Affiliations

Carlos Calvo Tapia1, Ivan Y. Tyukin2, and Valeri A. Makarov1,3,*

  • 1Instituto de Matemática Interdisciplinar, Faculty of Mathematics, Universidad Complutense de Madrid, Plaza Ciencias 3, 28040 Madrid, Spain
  • 2University of Leicester, Department of Mathematics, University Road, LE1 7RH, United Kingdom
  • 3Lobachevsky State University of Nizhny Novgorod, Gagarin Ave. 23, 603950 Nizhny Novgorod, Russia

  • *vmakarov@ucm.es

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Vol. 97, Iss. 5 — May 2018

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