Fast social-like learning of complex behaviors based on motor motifs
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909097365864448 |
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| author | Tapia, Carlos Calvo Tyukin, Ivan Y. Slizneva, Valeriy A. Makarov |
| author_facet | Tapia, Carlos Calvo Tyukin, Ivan Y. Slizneva, Valeriy A. Makarov |
| contents | 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 $(n-1)!$ possible sequences of motifs in a neural network, we employ the winner-less 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 $(n-1)$ 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 indeed the learner is 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_02226 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast social-like learning of complex behaviors based on motor motifs Tapia, Carlos Calvo Tyukin, Ivan Y. Slizneva, Valeriy A. Makarov Dynamical Systems Physics and Society 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 $(n-1)!$ possible sequences of motifs in a neural network, we employ the winner-less 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 $(n-1)$ 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 indeed the learner is 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. |
| title | Fast social-like learning of complex behaviors based on motor motifs |
| topic | Dynamical Systems Physics and Society |
| url | https://arxiv.org/abs/2402.02226 |