Abstract
This paper proposes and implements the Spiking Neural Network (SNN) with radio-frequency wireless communications. The implemented network could obtain the XOR function through reinforcement learning. By applying the wireless communication for Internet of Things to the SNN, the SNN works with sufficient communication distance and low power consumptions for not only the line of sight environment but also the non-line of sight one. Additionally, it is unnecessary to consider communication directivity and obstacles for constructing the networks. The experimental results showed the extensibility and the scalability of the implemented system in this paper.
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Hiraoka, R., Matsumoto, K., Nguyen, K., Torikai, H., Sekiya, H. (2019). Implementation of Spiking Neural Network with Wireless Communications. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_66
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DOI: https://doi.org/10.1007/978-3-030-36802-9_66
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