Research Article
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Year 2022, Volume 4, Issue 2, 88 - 93, 30.07.2022
https://doi.org/10.51537/chaos.1101581

Abstract

References

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  • Bao, B., Y. Zhu, J. Ma, H. Bao, H.Wu, et al., 2021 Memristive neuron model with an adapting synapse and its hardware experiments. Science China Technological Sciences 64: 1107–1117.
  • Bao, H., A. Hu,W. Liu, and B. Bao, 2019 Hidden bursting firings and bifurcation mechanisms in memristive neuron model with threshold electromagnetic induction. IEEE transactions on neural networks and learning systems 31: 502–511.
  • Fitzhugh, R., 1965 Mathematical models of excitation and propagation nerve. Biological Engineering .
  • Ge, M., Y. Jia, Y. Xu, and L. Yang, 2018 Mode transition in electrical activities of neuron driven by high and low frequency stimulus in the presence of electromagnetic induction and radiation. Nonlinear Dynamics 91: 515–523.
  • Hindmarsh, J. L. and R. Rose, 1984 A model of neuronal bursting using three coupled first order differential equations. Proceedings of the Royal society of London. Series B. Biological sciences 221: 87–102.
  • Hodgkin, A. L. and A. F. Huxley, 1952 A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of physiology 117: 500.
  • Izhikevich, E. M., 2003 Simple model of spiking neurons. IEEE Transactions on neural networks 14: 1569–1572.
  • Karata¸s, F., ˙I. Koyuncu, M. Tuna, M. Alçın, E. Avcioglu, et al., 2022 Design and implementation of arrhythmic ecg signals for biomedical engineering applications on fpga. The European Physical Journal Special Topics 231: 869–884.
  • Korkmaz, N. and R. Kilic, 2014 Implementations of modified chaotic neural models with analog reconfigurable hardware. International Journal of Bifurcation and Chaos 24: 1450046.
  • Korkmaz, N., ˙I. ÖZTÜRK, and R. Kilic, 2016 Multiple perspectives on the hardware implementations of biological neuron models and programmable design aspects. Turkish Journal of Electrical Engineering and Computer Sciences 24: 1729–1746.
  • Lin, H., C.Wang, Y. Sun, andW. Yao, 2020 Firing multistability in a locally active memristive neuron model. Nonlinear Dynamics 100: 3667–3683.
  • Linares-Barranco, B., E. Sánchez-Sinencio, Á. Rodríguez-Vazquez, and J. L. Huertas, 1991 A cmos implementation of fitzhughnagumo neuron model. IEEE Journal of Solid-State Circuits 26: 956–965.
  • Lv, M. and J. Ma, 2016 Multiple modes of electrical activities in a new neuron model under electromagnetic radiation. Neurocomputing 205: 375–381.
  • Lv, M., C. Wang, G. Ren, J. Ma, and X. Song, 2016 Model of electrical activity in a neuron under magnetic flow effect. Nonlinear Dynamics 85: 1479–1490.
  • Ma, J. and J. Tang, 2015 A review for dynamics of collective behaviors of network of neurons. Science China Technological Sciences 58: 2038–2045.
  • Ma, J., F. Wu, and C. Wang, 2017 Synchronization behaviors of coupled neurons under electromagnetic radiation. International Journal of Modern Physics B 31: 1650251.
  • Ma, J., G. Zhang, T. Hayat, and G. Ren, 2019 Model electrical activity of neuron under electric field. Nonlinear dynamics 95: 1585–1598.
  • Morris, C. and H. Lecar, 1981 Voltage oscillations in the barnacle giant muscle fiber. Biophysical journal 35: 193–213..
  • Sánchez-Sinencio, E. and B. Linares-Barranco, 1989 Circuit implementation of neural fitzhugh-nagumo equations. In Proceedings of the 32nd Midwest Symposium on Circuits and Systems,, pp. 244– 247, IEEE.
  • Wu, F., C. Wang, W. Jin, and J. Ma, 2017 Dynamical responses in a new neuron model subjected to electromagnetic induction and phase noise. Physica A: Statistical Mechanics and its Applications 469: 81–88.
  • Wu, F., C. Wang, Y. Xu, and J. Ma, 2016 Model of electrical activity in cardiac tissue under electromagnetic induction. Scientific reports 6: 1–12.
  • Xilinx, July, 2022 Xilinx inc. http://www.xilinx.com.
  • Xu, Y., H. Ying, Y. Jia, J. Ma, and T. Hayat, 2017 Autaptic regulation of electrical activities in neuron under electromagnetic induction. Scientific Reports 7: 1–12.

The FPGA-Based Realization of the Electromagnetic Effect Defined FitzHugh-Nagumo Neuron Model

Year 2022, Volume 4, Issue 2, 88 - 93, 30.07.2022
https://doi.org/10.51537/chaos.1101581

Abstract

The electrical transmission, which occurs on the surface of the neuron membranes, is based on the flow of charges such as calcium, potassium and sodium. This potential change means a current flow and if there is a variable current flow, a flux change comes into question. Accordingly, recent studies have suggested that these electrophysiological neuronal activities can induce a time-varying electromagnetic field distribution. The electric field is usually defined as an external stimulation variable of the biological neuron models in literature. However, the electric field is included in the biological neuron models as a new state variable in another perspective and it is described the polarization modultion of media. Here, this study focused on that the electric field is a state variable in the biological neuron model. The numerical simulations of the FitzHugh-Nagumo neuron, which is improved by including the electromagnetic effect, are re-executed in this study. Then, the hardware realization of this system is built on the FPGA device. Therefore, it is shown that it is also possible to perform the hardware realizations of the neuronal systems, which have a new state variable for the electric field definition.

References

  • Bao, B., A. Hu, H. Bao, Q. Xu, M. Chen, et al., 2018 Threedimensional memristive hindmarsh–rose neuron model with hidden coexisting asymmetric behaviors. Complexity 2018.
  • Bao, B., Y. Zhu, J. Ma, H. Bao, H.Wu, et al., 2021 Memristive neuron model with an adapting synapse and its hardware experiments. Science China Technological Sciences 64: 1107–1117.
  • Bao, H., A. Hu,W. Liu, and B. Bao, 2019 Hidden bursting firings and bifurcation mechanisms in memristive neuron model with threshold electromagnetic induction. IEEE transactions on neural networks and learning systems 31: 502–511.
  • Fitzhugh, R., 1965 Mathematical models of excitation and propagation nerve. Biological Engineering .
  • Ge, M., Y. Jia, Y. Xu, and L. Yang, 2018 Mode transition in electrical activities of neuron driven by high and low frequency stimulus in the presence of electromagnetic induction and radiation. Nonlinear Dynamics 91: 515–523.
  • Hindmarsh, J. L. and R. Rose, 1984 A model of neuronal bursting using three coupled first order differential equations. Proceedings of the Royal society of London. Series B. Biological sciences 221: 87–102.
  • Hodgkin, A. L. and A. F. Huxley, 1952 A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of physiology 117: 500.
  • Izhikevich, E. M., 2003 Simple model of spiking neurons. IEEE Transactions on neural networks 14: 1569–1572.
  • Karata¸s, F., ˙I. Koyuncu, M. Tuna, M. Alçın, E. Avcioglu, et al., 2022 Design and implementation of arrhythmic ecg signals for biomedical engineering applications on fpga. The European Physical Journal Special Topics 231: 869–884.
  • Korkmaz, N. and R. Kilic, 2014 Implementations of modified chaotic neural models with analog reconfigurable hardware. International Journal of Bifurcation and Chaos 24: 1450046.
  • Korkmaz, N., ˙I. ÖZTÜRK, and R. Kilic, 2016 Multiple perspectives on the hardware implementations of biological neuron models and programmable design aspects. Turkish Journal of Electrical Engineering and Computer Sciences 24: 1729–1746.
  • Lin, H., C.Wang, Y. Sun, andW. Yao, 2020 Firing multistability in a locally active memristive neuron model. Nonlinear Dynamics 100: 3667–3683.
  • Linares-Barranco, B., E. Sánchez-Sinencio, Á. Rodríguez-Vazquez, and J. L. Huertas, 1991 A cmos implementation of fitzhughnagumo neuron model. IEEE Journal of Solid-State Circuits 26: 956–965.
  • Lv, M. and J. Ma, 2016 Multiple modes of electrical activities in a new neuron model under electromagnetic radiation. Neurocomputing 205: 375–381.
  • Lv, M., C. Wang, G. Ren, J. Ma, and X. Song, 2016 Model of electrical activity in a neuron under magnetic flow effect. Nonlinear Dynamics 85: 1479–1490.
  • Ma, J. and J. Tang, 2015 A review for dynamics of collective behaviors of network of neurons. Science China Technological Sciences 58: 2038–2045.
  • Ma, J., F. Wu, and C. Wang, 2017 Synchronization behaviors of coupled neurons under electromagnetic radiation. International Journal of Modern Physics B 31: 1650251.
  • Ma, J., G. Zhang, T. Hayat, and G. Ren, 2019 Model electrical activity of neuron under electric field. Nonlinear dynamics 95: 1585–1598.
  • Morris, C. and H. Lecar, 1981 Voltage oscillations in the barnacle giant muscle fiber. Biophysical journal 35: 193–213..
  • Sánchez-Sinencio, E. and B. Linares-Barranco, 1989 Circuit implementation of neural fitzhugh-nagumo equations. In Proceedings of the 32nd Midwest Symposium on Circuits and Systems,, pp. 244– 247, IEEE.
  • Wu, F., C. Wang, W. Jin, and J. Ma, 2017 Dynamical responses in a new neuron model subjected to electromagnetic induction and phase noise. Physica A: Statistical Mechanics and its Applications 469: 81–88.
  • Wu, F., C. Wang, Y. Xu, and J. Ma, 2016 Model of electrical activity in cardiac tissue under electromagnetic induction. Scientific reports 6: 1–12.
  • Xilinx, July, 2022 Xilinx inc. http://www.xilinx.com.
  • Xu, Y., H. Ying, Y. Jia, J. Ma, and T. Hayat, 2017 Autaptic regulation of electrical activities in neuron under electromagnetic induction. Scientific Reports 7: 1–12.

Details

Primary Language English
Subjects Engineering, Electrical and Electronic
Journal Section Research Articles
Authors

Nimet KORKMAZ> (Primary Author)
Kayseri University
0000-0002-7419-1538
Türkiye


Bekir ŞIVGA>
KAYSERI UNIVERSITY
0000-0002-8373-2498
Türkiye

Early Pub Date July 30, 2022
Publication Date July 30, 2022
Published in Issue Year 2022, Volume 4, Issue 2

Cite

APA Korkmaz, N. & Şıvga, B. (2022). The FPGA-Based Realization of the Electromagnetic Effect Defined FitzHugh-Nagumo Neuron Model . Chaos Theory and Applications , 4 (2) , 88-93 . DOI: 10.51537/chaos.1101581

Chaos Theory and Applications in Applied Sciences and Engineering: An interdisciplinary journal of nonlinear science 23830