Abstract
In this paper, we propose a hybrid spiking neuron which can exhibit various bifurcation phenomena and response characteristics of inter spike intervals. Using a discrete/continuous-states hybrid map, we can clarify typical bifurcation mechanisms and can analyze the response characteristics. In addition, we propose a learning algorithm of the hybrid spiking neuron and show that the neuron can approximate given response characteristics of inter spike intervals.
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References
Izhikevich, E.M.: Dynamical systems in neuroscience. MIT Press, Cambridge (2006)
Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Transactions on Neuronal Networks 15(5), 1063–1069 (2004)
Nakano, H., Saito, T.: Grouping synchronization in a pulse-coupled network of chaotic spiking oscillators. IEEE Trans. Neural Networks 15(5), 1018–1026 (2004)
Torikai, H., Hamanaka, H., Saito, T.: Reconfigurable Digital Spiking Neuron and its Pluse-Coupled Network: Basic Characteristics and Potential Applications. IEEE Trans. CAS 53(8), 734–738 (2006)
Torikai, H., et al.: Digital spiking neuron and its learning for approximation of various spike-trains. Neural Networks (2008), doi:10.1016/j.neunet.2007.12.045
Torikai, H., Hashimoto, S.: A Hardware-oriented Learning Algorithm for a Digital Spiking Neuron. In: Proc. IEEE-INNS/ IJCNN (2008)
FPGA and HDL software package can be obtained, http://www.xilinx.com/
Devaney, R.L.: An Introduction to Chaotic Dynamical Systems, 2nd edn. Addison-Wesley Publishing Company, Reading (1995)
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Hashimoto, S., Torikai, H. (2009). A Novel Hybrid Spiking Neuron: Response Analysis and Learning Potential. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_18
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DOI: https://doi.org/10.1007/978-3-642-02490-0_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02489-4
Online ISBN: 978-3-642-02490-0
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