SpringerLink
Log in

A novel memristive neuron model and its energy characteristics

  • Research Article
  • Published:
Cognitive Neurodynamics Aims and scope Submit manuscript

Abstract

The functional neurons are basic building blocks of the nervous system and are responsible for transmitting information between different parts of the body. However, it is less known about the interaction between the neuron and the field. In this work, we propose a novel functional neuron by introducing a flux-controlled memristor into the FitzHugh-Nagumo neuron model, and the field effect is estimated by the memristor. We investigate the dynamics and energy characteristics of the neuron, and the stochastic resonance is also considered by applying the additive Gaussian noise. The intrinsic energy of the neuron is enlarged after introducing the memristor. Moreover, the energy of the periodic oscillation is larger than that of the adjacent chaotic oscillation with the changing of memristor-related parameters, and same results is obtained by varying stimuli-related parameters. In addition, the energy is proved to be another effective method to estimate stochastic resonance and inverse stochastic resonance. Furthermore, the analog implementation is achieved for the physical realization of the neuron. These results shed lights on the understanding of the firing mechanism for neurons detecting electromagnetic field.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

No data were used in the studies described in this article.

References

  • Chon KH, Scully CG, Lu S (2009) Approximate entropy for all signals. IEEE Eng Med Biol Mag 28:18–23

    Article  PubMed  Google Scholar 

  • Ding Q, Wu Y, Li T et al (2023) Metabolic energy consumption and information transmission of a two-compartment neuron model and its cortical network. Chaos Solitons Fractals 171:113464

    Article  Google Scholar 

  • Emlen ST, Wiltschko W, Demong NJ et al (1976) Magnetic direction finding: evidence for its use in migratory indigo buntings. Science 193:505–508

    Article  PubMed  CAS  Google Scholar 

  • FitzHugh R (1961) Impulses and physiological states in theoretical models of nerve membrane. Biophys J 1:445–466

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Gammaitoni L, Hänggi P, Jung P, Marchesoni F (1998) Stochastic resonance. Rev Mod Phys 70:223

    Article  CAS  Google Scholar 

  • Guo Y, Zhou P, Yao Z, Ma J (2021) Biophysical mechanism of signal encoding in an auditory neuron. Nonlinear Dyn 105:3603–3614

    Article  Google Scholar 

  • Guo Y, Wang C, Yao Z, Xu Y (2022) Desynchronization of thermosensitive neurons by using energy pum**. Physica A 602:127644

    Article  Google Scholar 

  • Guo Y, Wu F, Yang F, Ma J (2023) Physical approach of a neuron model with memristive membranes. Chaos: Interdiscip J Nonlinear Sci 33:113106

    Article  Google Scholar 

  • Herculano-Houzel S (2022) Energy supply per neuron is constrained by capillary density in the mouse brain. Front Integr Neurosci 16:760887

    Article  PubMed  PubMed Central  Google Scholar 

  • Hodgkin AL, Huxley AF, Katz B (1952) Measurement of current-voltage relations in the membrane of the giant axon of Loligo. J Physiol 116:424

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Hu Y, Ding Q, Wu Y, Jia Y (2023) Polarized electric field-induced drift of spiral waves in discontinuous cardiac media. Chaos Solitons Fractals 175:113957

    Article  Google Scholar 

  • Knight BW (1972) Dynamics of encoding in a population of neurons. J Gen Physiol 59:734–766

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kobe DH (1986) Helmholtz’s theorem revisited. Am J Phys 54:552–554

    Article  Google Scholar 

  • Koch C, Segev I (2000) The role of single neurons in information processing. Nat Neurosci 3:1171–1177

    Article  PubMed  CAS  Google Scholar 

  • Li T, Wu Y, Yang L et al (2023a) Neuronal morphology and network properties modulate signal propagation in multi-layer feedforward network. Chaos Solitons Fractals 172:113554

    Article  Google Scholar 

  • Li X, Yu D, Yang L et al (2023b) Energy dependence of synchronization mode transitions in the delay-coupled FitzHugh-Nagumo system driven by chaotic activity. Cogn Neurodyn. https://doi.org/10.1007/s11571-023-10021-9

    Article  PubMed  Google Scholar 

  • Li Z, Li C, **ong Z et al (2023c) Stochastic exceptional points for noise-assisted sensing. Phys Rev Lett 130:227201

    Article  PubMed  CAS  Google Scholar 

  • Liu Z, Han F, Wang Q (2022a) A review of computational models for gamma oscillation dynamics: from spiking neurons to neural masses. Nonlinear Dyn 108:1849–1866

    Article  Google Scholar 

  • Liu Z, Yu Y, Wang Q (2022b) Functional modular organization unfolded by chimera-like dynamics in a large-scale brain network model. Sci China Technol Sci 65:1435–1444

    Article  Google Scholar 

  • Lu L, Yi M, Gao Z et al (2023) Critical state of energy-efficient firing patterns with different bursting kinetics in temperature-sensitive Chay neuron. Nonlinear Dyn 111:16557–16567

    Article  Google Scholar 

  • Lv M, Ma J (2016) Multiple modes of electrical activities in a new neuron model under electromagnetic radiation. Neurocomputing 205:375–381

    Article  Google Scholar 

  • Lv M, Wang C, Ren G et al (2016) Model of electrical activity in a neuron under magnetic flow effect. Nonlinear Dyn 85:1479–1490

    Article  Google Scholar 

  • Majhi S, Perc M, Ghosh D (2022) Dynamics on higher-order networks: a review. J R Soc Interf 19:20220043

    Article  Google Scholar 

  • McDonnell MD, Abbott D (2009) What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology. PLoS Comput Biol 5:e1000348

    Article  PubMed  PubMed Central  Google Scholar 

  • McDonnell MD, Iannella N, To M-S et al (2015) A review of methods for identifying stochastic resonance in simulations of single neuron models. Netw: Comput Neural Syst 26:35–71

    Article  Google Scholar 

  • Mehrabbeik M, Jafari S, Perc M (2023) Synchronization in simplicial complexes of memristive Rulkov neurons. Front Comput Neurosci 17:1248976

    Article  PubMed  PubMed Central  Google Scholar 

  • Niven JE (2016) Neuronal energy consumption: biophysics, efficiency and evolution. Curr Opin Neurobiol 41:129–135

    Article  PubMed  CAS  Google Scholar 

  • Niven JE, Laughlin SB (2008) Energy limitation as a selective pressure on the evolution of sensory systems. J Exp Biol 211:1792–1804

    Article  PubMed  CAS  Google Scholar 

  • Njitacke ZT, Fozin TF, Muni SS et al (2022) Energy computation, infinitely coexisting patterns and their control from a Hindmarsh-Rose neuron with memristive autapse: circuit implementation. AEU-Int J Electr Commun 155:154361

    Article  Google Scholar 

  • Njitacke ZT, Ramadoss J, Takembo CN et al (2023) An enhanced FitzHugh–Nagumo neuron circuit, microcontroller-based hardware implementation: light illumination and magnetic field effects on information patterns. Chaos Solitons Fractals 167:113014

    Article  Google Scholar 

  • Parastesh F, Jafari S, Azarnoush H et al (2021) Chimeras. Phys Rep 898:1–114

    Article  Google Scholar 

  • Parastesh F, Mehrabbeik M, Rajagopal K et al (2022) Synchronization in Hindmarsh-Rose neurons subject to higher-order interactions. Chaos: Interdiscip J Nonlinear Sci 32:013125

    Article  Google Scholar 

  • Saaty T (2017) Neurons the decision makers, Part I: the firing function of a single neuron. Neural Netw 86:102–114

    Article  PubMed  Google Scholar 

  • Sacu IE (2023) Dynamics and synchronization control of fractional conformable neuron system. Cogn Neurodyn. https://doi.org/10.1007/s11571-023-09933-3

    Article  Google Scholar 

  • Shen R, Wang B, Giribaldi MG et al (2016) Neuronal energy-sensing pathway promotes energy balance by modulating disease tolerance. Proc Natl Acad Sci 113:E3307–E3314

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Shoeibi A, Ghassemi N, Khodatars M et al (2022) Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression. Cogn Neurodyn 17:1501–1523

    Article  PubMed  Google Scholar 

  • Stam R (2010) Electromagnetic fields and the blood–brain barrier. Brain Res Rev 65:80–97

    Article  PubMed  Google Scholar 

  • Sun G, Yang F, Ren G, Wang C (2023) Energy encoding in a biophysical neuron and adaptive energy balance under field coupling. Chaos Solitons Fractals 169:113230

    Article  Google Scholar 

  • Tuckwell HC, Jost J (2012) Analysis of inverse stochastic resonance and the long-term firing of Hodgkin-Huxley neurons with Gaussian white noise. Physica A 391:5311–5325

    Article  Google Scholar 

  • Wang G, Xu Y, Ge M et al (2020) Mode transition and energy dependence of FitzHugh-Nagumo neural model driven by high-low frequency electromagnetic radiation. AEU-Int J Electr Commun 120:153209

    Article  Google Scholar 

  • Wang X, Yu D, Li T, Jia Y (2023a) Logistic stochastic resonance in the Hodgkin-Huxley neuronal system under electromagnetic induction. Physica A 630:129247

    Article  Google Scholar 

  • Wang X, Yu D, Wu Y et al (2023b) Effects of potassium channel blockage on inverse stochastic resonance in Hodgkin-Huxley neural systems. J Zhejiang Univ-Sci A 24:735–748

    Article  Google Scholar 

  • Wu Y, Ding Q, Li T et al (2023) Effect of temperature on synchronization of scale-free neuronal network. Nonlinear Dyn 111:2693–2710

    Article  Google Scholar 

  • **e Y, Yao Z, Ma J (2022) Phase synchronization and energy balance between neurons. Front Inf Technol Electr Eng 23:1407–1420

    Article  Google Scholar 

  • **e Y, Yao Z, Ma J (2023a) Formation of local heterogeneity under energy collection in neural networks. Sci China Technol Sci 66:439–455

    Article  Google Scholar 

  • **e Y, Zhou P, Ma J (2023b) Energy balance and synchronization via inductive-coupling in functional neural circuits. Appl Math Model 113:175–187

    Article  Google Scholar 

  • Xu Q, Ju Z, Ding S et al (2022) Electromagnetic induction effects on electrical activity within a memristive Wilson neuron model. Cogn Neurodyn 16:1221–1231

    Article  PubMed  PubMed Central  Google Scholar 

  • Yang F, Guo Q, Ma J (2023) A neuron model with nonlinear membranes. Cogn Neurodyn. https://doi.org/10.1007/s11571-023-10017-5

    Article  PubMed  Google Scholar 

  • Yao Z, Sun K, He S (2022) Firing patterns in a fractional-order FithzHugh–Nagumo neuron model. Nonlinear Dyn 110:1807–1822

    Article  Google Scholar 

  • Yao Z, Sun K, He S (2023) Synchronization in fractional-order neural networks by the energy balance strategy. Cogn Neurodyn. https://doi.org/10.1007/s11571-023-10023-7

    Article  PubMed  Google Scholar 

  • Yu D, Wang G, Li T et al (2023a) Filtering properties of Hodgkin-Huxley neuron on different time-scale signals. Commun Nonlinear Sci Numer Simul 117:106894

    Article  Google Scholar 

  • Yu D, Zhan X, Yang L, Jia Y (2023b) Theoretical description of logical stochastic resonance and its enhancement: fast fourier transform filtering method. Phys Rev E 108:014205

    Article  PubMed  CAS  Google Scholar 

  • Yu X, Bao H, Chen M, Bao B (2023c) Energy balance via memristor synapse in Morris-Lecar two-neuron network with FPGA implementation. Chaos Solitons Fractals 171:113442

    Article  Google Scholar 

  • Yu Y, Fan Y, Han F et al (2023d) Transcranial direct current stimulation inhibits epileptic activity propagation in a large-scale brain network model. Sci China Technol Sci 66:3628–3638

    Article  CAS  Google Scholar 

  • Yuste R (2015) From the neuron doctrine to neural networks. Nat Rev Neurosci 16:487–497

    Article  PubMed  CAS  Google Scholar 

  • Zang Y, Marder E (2023) Neuronal morphology enhances robustness to perturbations of channel densities. Proc Natl Acad Sci 120:e2219049120

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zhai Z-M, Kong L-W, Lai Y-C (2023) Emergence of a resonance in machine learning. Phys Rev Res 5:033127

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work is supported by National Natural Science Foundation of China under No. 12175080, also financially supported by self-determined research funds of CCNU from the colleges’ basic research and operation of MOE under No. CCNU22JC009.

Author information

Authors and Affiliations

  1. Department of Physics, Central China Normal University, Wuhan, 430079, China

    Ying **e, Zhiqiu Ye, Xuening Li, Xueqin Wang & Ya Jia

Authors
  1. Ying **e
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiqiu Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuening Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xueqin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ya Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ya Jia.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

**e, Y., Ye, Z., Li, X. et al. A novel memristive neuron model and its energy characteristics. Cogn Neurodyn (2024). https://doi.org/10.1007/s11571-024-10065-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11571-024-10065-5

Keywords

Search

Navigation