Abstract
This paper presents a ladder-type digital spiking neural network and its hardware implementation. Depending on the parameters, the network can exhibit multi-phase synchronization of periodic spike-trains. Applying a time dependent selection switching, the network can output a variety of periodic spike-trains consisting of any combination of desired inter-spike-intervals. The network is a digital dynamical system and is suitable for FPGA based hardware implementation. A test circuit is implemented in a FPGA board by the Verilog and typical phenomena are confirmed experimentally. These results will be developed into several applications including time-series approximation/prediction.
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Izhikevich, E.M.: Dynamical Systems in Neuroscience. MIT Press, Cambridge (2006)
Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Netw. 14(6), 1569–1572 (2003)
Perez, R., Glass, L.: Bistability, period doubling bifurcations and chaos in a periodically forced oscillator. Phys. Lett. A 90(9), 441–443 (1982)
Campbell, S.R., Wang, D., Jayaprakash, C.: Synchrony and desynchrony in integrate-and-fire oscillators. Neural Comput. 11, 1595–1619 (1999)
Rulkov, N.F., Sushchik, M.M., Tsimring, L.S., Volkovskii, A.R.: Digital communication using chaotic-pulse-position modulation. IEEE Trans. Circuits Syst. 48(12), 1436–1444 (2001)
Iguchi, T., Hirata, A., Torikai, H.: Theoretical and heuristic synthesis of digital spiking neurons for spike-pattern-division multiplexing. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E93–A(8), 1486–1496 (2010)
Lozano, A., Rodriguez, M., Roberto Barrio, R.: Control strategies of 3-cell Central Pattern Generator via global stimuli. Sci. Rep. 6, 23622 (2016)
Torikai, H., Hamanaka, H., Saito, T.: Reconfigurable spiking neuron and its pulse-coupled networks: basic characteristics and potential applications. IEEE Trans. Circuits Syst. II 53(8), 734–738 (2006)
Torikai, H., Saito, T., Schwarz, W.: Synchronization via multiplex pulse trains. IEEE Trans. Circuits Syst. I 46(9), 1072–1085 (1999)
Torikai, H., Funew, A., Saito, T.: Digital spiking neuron and its learning for approximation of various spike-trains. Neural Netw. 21, 140–149 (2008)
Saito, T., Yamaoka, K., Hamaguchi, T.: Realization of desired digital spike-trains by a simple evolutionary algorithm. NOLTA, IEICE E8–N(4), 267–278 (2017)
Uchida, H., Saito, T.: Implementation of desired digital spike maps in the digital spiking neurons. In: Liu, D., et al. (eds.) Neural Information Processing – ICONIP 2017. LNCS, vol. 10639, pp. 804–811. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70136-3_85
Appeltant, L., et al.: Information processing using a single dynamical node as complex system. Nat. Commun. 2, 468 (2011)
Antonik, P., Hermans, M., Haeltermany, M., Massar, S.: Photonic reservoir computer with output feedback for chaotic time series prediction. In: Proceedings of IJCNN, pp. 2407–2413 (2017)
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Uchida, H., Saito, T. (2018). A Ladder-Type Digital Spiking Neural Network. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11301. Springer, Cham. https://doi.org/10.1007/978-3-030-04167-0_50
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DOI: https://doi.org/10.1007/978-3-030-04167-0_50
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