Abstract
Chaotic spiking neural network serves as a main component (a “liquid”) in liquid state machines (LSM) – a very promising approach to application of neural networks to online analysis of dynamic data streams. The LSM ability to recognize complex dynamic patterns is based on “memory” of its liquid component – prolonged reaction of its neural network to input stimuli. A generalization of LSM called self-organizing LSM (LSM including spiking neural network with synaptic plasticity switched on) is studied. It is demonstrated that memory appears in such networks under certain locality conditions on their connectivity. Genetic algorithm is utilized to determine parameters of neuron model, synaptic plasticity rule and connectivity optimal from point of view of memory characteristics.
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Acknowledgements
I would like to thank Andrey Lavrentyev and Artyom Nechiporuk for valuable discussion. I am grateful to Kaspersky Lab for the powerful GPU computer provided.
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Kiselev, M. (2020). Chaotic Spiking Neural Network Connectivity Configuration Leading to Memory Mechanism Formation. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research III. NEUROINFORMATICS 2019. Studies in Computational Intelligence, vol 856. Springer, Cham. https://doi.org/10.1007/978-3-030-30425-6_47
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DOI: https://doi.org/10.1007/978-3-030-30425-6_47
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