Abstract
A Liquid State Machine (LSM) is trained to model the stochastic behavior of a cockroach exploring an unknown environment. The LSM is a recurrent neural network of leaky-integrate-and-fire neurons interconnected by synapses with intrinsic dynamics and outputs to an Artificial Neural Network (ANN). The LSM is trained by a reinforcement approach to produce a probability distribution over a discrete control space which is then sampled by the controller to determine the next course of action. The LSM is able to capture several observed phenomenon of cockroach exploratory behavior including resting under shelters and wall following.
This work is based upon work support by\(_{\rm }^{\rm * }\)Defense Advanced Research Projects Agency (DARPA) Maximum Mobility and Manipulation (M3) research grant No. DI-MISC-81612A and by the National Science Foundation (NSF) under grant No. IIS-1065489. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of either DARPA or NSF.
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Lonsberry, A., Daltorio, K., Quinn, R.D. (2014). Capturing Stochastic Insect Movements with Liquid State Machines. In: Duff, A., Lepora, N.F., Mura, A., Prescott, T.J., Verschure, P.F.M.J. (eds) Biomimetic and Biohybrid Systems. Living Machines 2014. Lecture Notes in Computer Science(), vol 8608. Springer, Cham. https://doi.org/10.1007/978-3-319-09435-9_17
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DOI: https://doi.org/10.1007/978-3-319-09435-9_17
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