Abstract
During the release and propagation of intracellular and extracellular ions, electromagnetic field is induced accompanying with propagation of energy flow. The firing mode is dependent on the energy level, and external energy injection will induce distinct mode transition. Exact energy function for a neuron developed from a neural circuit can be obtained directly by applying scale transformation for the physical field energy. For generic neuron models, dimensionless Hamilton energy function can be obtained by using Helmholtz theorem, and this energy function can be considered as a specific Lyapunov function. In this review, approach of energy function for memristive neuron is discussed by designing equivalent neural circuit coupled by two kinds of memristors, which are dependent on the magnetic flux and charge flux, respectively. A scheme is suggested to get equivalent energy function for memristive neuron in the form of map by introducing a scale parameter. The memristive map reduced from the memristive neuron can produce similar attractors and firing modes under applying the same parameters, and the average Hamilton energy for the map neuron is decreased because of regulation from the scale parameter. On the other hand, a memristive map is replaced by an equivalent memristive oscillator for finding an equivalent Hamilton energy function according to the Helmholtz theorem. The energy scheme can be helpful for further investigating energy property of artificial neurons, maps and discrete memristors. It also provides evidence that maps are more suitable for investigating neural activities than neuron oscillators.
Similar content being viewed by others
Data availability
The data are available upon reasonable request.
References
Kavehei, O., Iqbal, A., Kim, Y.S., et al.: The fourth element: characteristics, modelling and electromagnetic theory of the memristor. Proc. Royal Soc. A: Math. Phys. Eng. Sci. 466, 2175–2202 (2010)
Yeşil, A., Babacan, Y., Kaçar, F.: A new DDCC based memristor emulator circuit and its applications. Microelectron. J. 45, 282–287 (2014)
Zidan, M.A., Fahmy, H.A.H., Hussain, M.M., et al.: Memristor-based memory: the sneak paths problem and solutions. Microelectron. J. 44, 176–183 (2013)
Wang, L., Yang, C.H., Wen, J., et al.: Overview of emerging memristor families from resistive memristor to spintronic memristor. J. Mater. Sci.: Mater. Electron. 26, 4618–4628 (2015)
Goswami, S., Pramanick, R., Patra, A., et al.: Decision trees within a molecular memristor. Nature 597, 51–56 (2021)
Kuznetsov, N., Mokaev, T., Ponomarenko, V., et al.: Hidden attractors in Chua circuit: mathematical theory meets physical experiments. Nonlinear Dyn. 111, 5859–5887 (2023)
Wang, E., Yan, S., Sun, X., et al.: Analysis of bifurcation mechanism of new hyperchaotic system, circuit implementation, and synchronization. Nonlinear Dyn. 111, 3869–3885 (2023)
Vijay, S.D., Thamilmaran, K., Ahamed, A.I.: Superextreme spiking oscillations and multistability in a memristor-based Hindmarsh-Rose neuron model. Nonlinear Dyn. 111, 789–799 (2023)
Xu, H., Zhang, Z., Peng, M.: Novel bursting patterns and the bifurcation mechanism in a piecewise smooth Chua’s circuit with two scales. Nonlinear Dyn. 108, 1755–1771 (2022)
Zhang, X., Ma, J.: Wave filtering and firing modes in a light-sensitive neural circuit. J. Zhejiang Univ. Sci. A 9, 707–720 (2021)
Guan, M.J., Liao, W.H.: On the equivalent circuit models of piezoelectric ceramics. Ferroelectrics 386, 77–87 (2009)
Karthikeyan, A., Cimen, M.E., Akgul, A., et al.: Persistence and coexistence of infinite attractors in a fractal Josephson junction resonator with unharmonic current phase relation considering feedback flux effect. Nonlinear Dyn. 103, 1979–1998 (2021)
Zhang, Y., Xu, Y., Yao, Z., et al.: A feasible neuron for estimating the magnetic field effect. Nonlinear Dyn. 102, 1849–1867 (2020)
Xie, Y., Yao, Z., Hu, X., et al.: Enhance sensitivity to illumination and synchronization in light-dependent neurons. Chin. Phys. B 30, 120510 (2021)
Liu, Y., Xu, W., Ma, J., et al.: A new photosensitive neuron model and its dynamics. Front. Informa. Technol. Electron. Eng. 21, 1387–1396 (2020)
Ibrahim, O., Hassan, S.M., Abdulkarim, A., et al.: Design of wheatstone bridge based thermistor signal conditioning circuit for temperature measurement. J. Eng. Sci. Technol. Rev. 12, 12–17 (2019)
Naveen Kumar, V., Lakshmi Narayana, K.V.: Development of thermistor signal conditioning circuit using artificial neural networks. IET Sci. Meas. Technol. 9, 955–961 (2015)
Groschner, L.N., Malis, J.G., Zuidinga, B., et al.: A biophysical account of multiplication by a single neuron. Nature 603, 119–123 (2022)
Ma, J.: Biophysical neurons, energy, and synapse controllability: a review. J. Zhejiang Univ. Sci. A 24, 109–129 (2023)
Wu, F., Hu, X., Ma, J.: Estimation of the effect of magnetic field on a memristive neuron. Appl. Math. Comput. 432, 127366 (2022)
Wu, F., Ma, J., Zhang, G.: A new neuron model under electromagnetic field. Appl. Math. Comput. 347, 590–599 (2019)
Di Maio, V., Santillo, S., Ventriglia, F.: Synaptic dendritic activity modulates the single synaptic event. Cogn. Neurodyn. 15, 279–297 (2021)
Wang, C., Yao, Z., Xu, W., et al.: Phase synchronization between nonlinear circuits by capturing electromagnetic field energy. Mod. Phys. Lett. B 34, 2050323 (2020)
Zhou, P., Ma, J., Tang, J.: Clarify the physical process for fractional dynamical systems. Nonlinear Dyn. 100, 2353–2364 (2020)
Zhou, P., Zhang, X., Hu, X., et al.: Energy balance between two thermosensitive circuits under field coupling. Nonlinear Dyn. 110, 1879–1895 (2022)
Ren, L., Lin, M.H., Abdulwahab, A., et al.: Global dynamical analysis of the integer and fractional 4D hyperchaotic Rabinovich system. Chaos, Solit. Fract. 169, 113275 (2023)
Leutcho, G.D., Khalaf, A.J.M., Njitacke Tabekoueng, Z., et al.: A new oscillator with mega-stability and its Hamilton energy: infinite coexisting hidden and self-excited attractors. Chaos 30, 033112 (2020)
Njitacke, Z.T., Takembo, C.N., Awrejcewicz, J., et al.: Hamilton energy, complex dynamical analysis and information patterns of a new memristive FitzHugh-Nagumo neural network. Chaos, Solit. Fract. 160, 112211 (2022)
Rong, K., Bao, H., Li, H., et al.: Memristive Hénon map with hidden Neimark-Sacker bifurcations. Nonlinear Dyn. 108, 4459–4470 (2022)
Peng, Y., Sun, K., He, S.: A discrete memristor model and its application in Hénon map. Chaos, Solitons Fractals 137, 109873 (2020)
Fouda, M.E., Radwan, A.G.: Charge controlled memristor-less memcapacitor emulator. Electron. Lett. 48, 1454–1455 (2012)
Petrović, P.B.: Charge-controlled grounded memristor emulator circuits based on Arbel-Goldminz cell with variable switching behaviour. Analog Integr. Circ. Sig. Process 113, 373–381 (2022)
Yang, F., Xu, Y., Ma, J.: A memristive neuron and its adaptability to external electric field. Chaos 33, 023110 (2023)
Lin, R., Shi, G., Qiao, F., et al.: Research progress and applications of memristor emulator circuits. Microelectron. J. 133, 105702 (2023)
Yao, Z., Zhou, P., Alsaedi, A., et al.: Energy flow-guided synchronization between chaotic circuits. Appl. Math. Comput. 374, 124998 (2020)
Bao, B., Hu, J., Cai, J., et al.: Memristor-induced mode transitions and extreme multistability in a map-based neuron model. Nonlinear Dyn. 111, 3765–3779 (2023)
Vijayakumar, M.D., Natiq, H., Meli, M.I.T., et al.: Hamiltonian energy computation of a novel memristive mega-stable oscillator (MMO) with dissipative, conservative and repelled dynamics. Chaos, Solit. Fract. 155, 111765 (2022)
Wang, G., Xu, Y., Ge, M., et al.: Mode transition and energy dependence of FitzHugh-Nagumo neural model driven by high-low frequency electromagnetic radiation. AEU-Int. J. Electron. Commun. 120, 153209 (2020)
Xie, Y., Zhou, P., Ma, J.: Energy balance and synchronization via inductive-coupling in functional neural circuits. Appl. Math. Model. 113, 175–187 (2023)
Kobe, D.H.: Helmholtz’s theorem revisited. Am. J. Phys. 54, 552–554 (1986)
Song, F., Liu, Y., Shen, D., et al.: Learning control for motion coordination in wafer scanners: toward gain adaptation. IEEE Trans. Industr. Electron. 69, 13428–13438 (2022)
Zhang, C., Kordestani, H., Shadabfar, M.: A combined review of vibration control strategies for high-speed trains and railway infrastructures: challenges and solutions. J. Low Freq. Noise Vib. Active Control 42, 272–291 (2023)
Li, D., Yu, H., Tee, K.P., et al.: On time-synchronized stability and control. IEEE Trans. Syst. Man Cyber. Syst. 52, 2450–2463 (2021)
Wang, R., Wang, Y., Xu, X., et al.: Brain works principle followed by neural information processing: a review of novel brain theory. Artif. Intell. Rev. (2023). https://doi.org/10.1007/s10462-023-10520-5
Xia, C., Zhu, Y., Zhou, S., et al.: Simulation study on transient performance of a marine engine matched with high-pressure SCR system. Int. J. Engine Res. 24, 1327–1345 (2023)
Yang, F., Wang, Y., Ma, J.: Creation of heterogeneity or defects in a memristive neural network under energy flow. Commun. Nonlinear Sci. Numer. Simul. 119, 107127 (2023)
Xie, Y., Yao, Z., Ma, J.: Formation of local heterogeneity under energy collection in neural networks. Sci. China Technol. Sci. 66, 439–455 (2023)
Shen, B. W., Pielke, R. A. Sr, Zeng, X.: The 50th Anniversary of the Metaphorical Butterfly Effect since Lorenz (1972): Multistability, Multiscale Predictability, and Sensitivity in Numerical Models. Atmosphere, 14(8): 1279 (2023)
Acknowledgements
This project is supported by the National Natural Science Foundation of China under Grant Nos. 12072139.
Funding
The authors have not disclosed any funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest with this publication.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Guo, Y., Xie, Y. & Ma, J. How to define energy function for memristive oscillator and map. Nonlinear Dyn 111, 21903–21915 (2023). https://doi.org/10.1007/s11071-023-09039-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11071-023-09039-9