Abstract
An active sensory system is designed using the Markov chain Monte Carlo (MCMC) method. The system is a feedback circuit comprising four parts: receptors, sensory, motor, and internal neurons. The neurons are modeled using excitable cellular automata. The neural connections between them are determined by the MCMC method so as to minimize a given sensing error. Several modes of sensing motions have been found depending on the optimization level, and a logical interpretation of an optimized neural network is obtained.
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Acknowledgements
This study has been partially supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT, Japan) , Grant-in-Aid for Scientific Research (Grant No. 21540376, 22120501), and the Volkswagen Foundation (Germany).
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Yanagita, T. (2013). Design and Dynamics of Active-Touch Sensory Model. In: Yamaguchi, Y. (eds) Advances in Cognitive Neurodynamics (III). Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4792-0_78
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DOI: https://doi.org/10.1007/978-94-007-4792-0_78
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