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An Izhikevich Model Neuron MOS Circuit for Low Voltage Operation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11727))

Abstract

The Izhikevich neuron model has attracted attention because it can reproduce various neural activities although it is described by simple differential equations and is expected to be applied to engineering. Among a few MOS circuits inspired by the Izhikevich model, the circuit proposed by Wijekoon and Dudek in 2008 exhibits the simplest structure, and it is practical. However, the power supply voltage of the circuit is 3.3 V. To implement such a neuron MOS circuit using state-of-the-art semiconductor manufacturing process, we must redesign the circuit to operate it with a lower supply voltage. Thus, we analyzed their circuit operation by SPICE simulation assuming a 1.0 V supply voltage and found that the bias voltage ranges to generate specific spike activities were limited. In addition, we clarified the discrepancies between the Izhikevich neuron model and the original circuit. In this study, we propose a new Izhikevich model neuron circuit based on these findings and investigate the circuit dynamics by null-cline analysis and SPICE simulation. The dynamics of the proposed MOS circuit are close to those of the Izhikevich model and various spikes are generated. Furthermore, we successfully enlarged the bias voltage range for specific spikes.

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References

  1. Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Networks 14(6), 1569–1572 (2003). https://doi.org/10.1109/TNN.2003.820440

    Article  MathSciNet  Google Scholar 

  2. Jayawan, H.B. Wijekoon, P.D.: Compact silicon neuron circuit with spiking and bursting behavior. Neural Networks 21, 524–534 (2008). https://doi.org/10.1016/j.neunet.2007.12.037

    Article  Google Scholar 

  3. Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Trans. Neural Networks 15(5), 1063–1070 (2004). https://doi.org/10.1109/TNN.2004.832719

    Article  Google Scholar 

  4. Brette, R., Gerstner, W.: Adaptive exponential Integrate-and-Fire model as an effective description of neuronal activity. J. Neurophysiol. 94(5), 3637–3642 (2005). https://doi.org/10.1152/jn.00686.2005

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Acknowledgments

This study was supported by the Cooperative Research Project Program of the Research Institute of Electrical Communication, Tohoku University; the Program on Open Innovation Platform with Enterprises, Research Institute and Academia (OPERA) from Japan Science and Technology Agency (JST); JSPS KAKENHI (Grant Nos, 17K18864 and 18J12197); and JST CREST Grant Number JPMJCR18K4, Japan.

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Correspondence to Yuki Tamura .

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Tamura, Y., Moriya, S., Kato, T., Sakuraba, M., Horio, Y., Sato, S. (2019). An Izhikevich Model Neuron MOS Circuit for Low Voltage Operation. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. ICANN 2019. Lecture Notes in Computer Science(), vol 11727. Springer, Cham. https://doi.org/10.1007/978-3-030-30487-4_55

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  • DOI: https://doi.org/10.1007/978-3-030-30487-4_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30486-7

  • Online ISBN: 978-3-030-30487-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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