journal article·2023-02

Formation of local heterogeneity under energy collection in neural networks

Ying Xie, Zhao Yao, Jun Ma

Publisher page

Abstract

Static distribution of intracellular and extracellular ions can induce the spatial distribution of electric field in the cell, while stochastic diffusion and propagation of ions can generate channel current accompanied by the magnetic field. In fact, the field energy (including magnetic and electric fields) in each cell or neuron can be changed under external stimuli or the deformation of shape. Furthermore, energy pumping occurs, and the synaptic connection is created adaptively to keep energy balance when more neurons are clustered in the same region because of electromagnetic field superposition. For a synchronous and homogeneous network, all neurons keep energy balance with the adjacent neurons, and the network becomes uniform and identical. For cardiac tissue and biological media, local heterogeneity such as sinoatrial node can emit continuous wave front, and target waves are propagated to regulate the neural activities and heartbeat rhythm. In this paper, the Hamilton energy in a simple neuron model is calculated according to the Helmholtz theorem, and more neurons are clustered to build a regular network (chain network and square array). The creation and growth of synapse connection are controlled to pump energy, and local heterogeneity is formed by taming one intrinsic parameter when more energy is accumulated in a few neurons in the network. These results indicate that asymmetrical energy transport will induce the occurrence of heterogeneity in the network, and adaptive synaptic regulation on neurons is activated to keep local energy balance before reaching perfect synchronization. When external stimuli with diversity in intensity are applied, neurons are excited with energy diversity, and possible shape deformation is induced to keep local energy balance. As a result, heterogeneity is developed in a local area.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Neural dynamics and brain function · stochastic dynamics and bifurcation · Photoreceptor and optogenetics research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (66)

Show source-provided references
1. Treibel T A, White S K, Moon J C. Myocardial tissue characterization: Histological and pathophysiological correlation. Curr Cardiovasc Imag Rep, 2014, 7: 925410.1007/s12410-013-9254-9 ↗
2. Ramadan S, Paul N, Naguib H E. Standardized static and dynamic evaluation of myocardial tissue properties. Biomed Mater, 2017, 12: 02501310.1088/1748-605X/aa57a5 ↗
3. Nerheim P, Birger-Botkin S, Piracha L, et al. Heart failure and sudden death in patients with tachycardia-induced cardiomyopathy and recurrent tachycardia. Circulation, 2004, 110: 247–25210.1161/01.CIR.0000135472.28234.CC ↗
4. Göktepe S, Wong J, Kuhl E. Atrial and ventricular fibrillation: Computational simulation of spiral waves in cardiac tissue. Arch Appl Mech, 2010, 80: 569–58010.1007/s00419-009-0384-0 ↗
5. Cherry E M, Fenton F H. Visualization of spiral and scroll waves in simulated and experimental cardiac tissue. New J Phys, 2008, 10: 12501610.1088/1367-2630/10/12/125016 ↗
6. Chen J X, Xiao J, Qiao L Y, et al. Dynamics of scroll waves with time-delay propagation in excitable media. Commun Nonlinear Sci Numer Simul, 2018, 59: 331–33710.1016/j.cnsns.2017.11.014 ↗
7. Chen J X, Zhang H, Qiao L Y, et al. Interaction of excitable waves emitted from two defects by pulsed electric fields. Commun Nonlinear Sci Numer Simul, 2018, 54: 202–20910.1016/j.cnsns.2017.05.034 ↗
8. Eason J, Trayanova N. Phase singularities and termination of spiral wave reentry. J Cardiovasc Electrophysiol, 2002, 13: 672–67910.1046/j.1540-8167.2002.00672.x ↗
9. Aron M, Herzog S, Parlitz U, et al. Spontaneous termination of chaotic spiral wave dynamics in human cardiac ion channel models. PLoS ONE, 2019, 14: e022140110.1371/journal.pone.0221401 ↗
10. Hussaini S, Venkatesan V, Biasci V, et al. Drift and termination of spiral waves in optogenetically modified cardiac tissue at sub-threshold illumination. eLife, 2021, 10: e5995410.7554/eLife.59954 ↗
11. Sato S. Mathematical properties of responses of a neuron model. Kybernetik, 1972, 11: 208–21610.1007/BF00271697 ↗
12. Buchholtz F, Golowasch J, Epstein I R, et al. Mathematical model of an identified stomatogastric ganglion neuron. J Neurophysiol, 1992, 67: 332–34010.1152/jn.1992.67.2.332 ↗
13. Knight B W. Dynamics of encoding in neuron populations: Some general mathematical features. Neural Computat, 2000, 12: 473–51810.1162/089976600300015673 ↗
14. Kaushik A, Sharma M D. Numerical analysis of a mathematical model for propagation of an electrical pulse in a neuron. Numer Methods Partial Differ Eq, 2008, 24: 1055–107910.1002/num.20301 ↗
15. Popovych S, Gail A, Schropp J. Complex dynamics of a single neuron model. Phys Rev E, 2006, 74: 04191410.1103/PhysRevE.74.041914 ↗
16. Mishra D, Yadav A, Ray S, et al. Exploring biological neuron models. Directions, The Research Magazine of IIT Kanpur, 2006, 7: 13–22
17. Conte E, Pierri G P, Federici A, et al. A model of biological neuron with terminal chaos and quantum-like features. Chaos Solitons Fractals, 2006, 30: 774–78010.1016/j.chaos.2005.08.211 ↗
18. Fu X, Yu Y. Reliable and efficient processing of sensory information at body temperature by rodent cortical neurons. Nonlinear Dyn, 2019, 98: 215–23110.1007/s11071-019-05184-2 ↗
19. Ditlevsen S, Greenwood P. The Morris-Lecar neuron model embeds a leaky integrate-and-fire model. J Math Biol, 2013, 67: 239–25910.1007/s00285-012-0552-7 ↗
20. Zhu F, Wang R, Aihara K, et al. Energy-efficient firing patterns with sparse bursts in the Chay neuron model. Nonlinear Dyn, 2020, 100: 2657–267210.1007/s11071-020-05593-8 ↗
21. Song X, Wang H, Chen Y. Autapse-induced firing patterns transitions in the Morris-Lecar neuron model. Nonlinear Dyn, 2019, 96: 2341–235010.1007/s11071-019-04925-7 ↗
22. Zhao Z, Li L, Gu H, et al. Different dynamics of repetitive neural spiking induced by inhibitory and excitatory autapses near subcritical Hopf bifurcation. Nonlinear Dyn, 2020, 99: 1129–115410.1007/s11071-019-05342-6 ↗
23. Yue Y, Liu L, Liu Y, et al. Dynamical response, information transition and energy dependence in a neuron model driven by autapse. Non-linear Dyn, 2017, 90: 2893–290210.1007/s11071-017-3850-1 ↗
24. Wang C, Guo S, Xu Y, et al. Formation of autapse connected to neuron and its biological function. Complexity, 2017, 2017: 543673710.1155/2017/5436737 ↗
25. Qu L, Du L, Cao Z, et al. Pattern transition of neuronal networks induced by chemical autapses with random distribution. Chaos Solitons Fractals, 2021, 144: 11064610.1016/j.chaos.2020.110646 ↗
26. Yilmaz E, Ozer M, Baysal V, et al. Autapse-induced multiple coherence resonance in single neurons and neuronal networks. Sci Rep, 2016, 6: 3091410.1038/srep30914 ↗
27. Qin H, Wu Y, Wang C, et al. Emitting waves from defects in network with autapses. Commun Nonlinear Sci Numer Simul, 2015, 23: 164–17410.1016/j.cnsns.2014.11.008 ↗
28. Ma J, Song X, Tang J, et al. Wave emitting and propagation induced by autapse in a forward feedback neuronal network. Neurocomputing, 2015, 167: 378–38910.1016/j.neucom.2015.04.056 ↗
29. Yao C, He Z, Nakano T, et al. Inhibitory-autapse-enhanced signal transmission in neural networks. Nonlinear Dyn, 2019, 97: 1425–143710.1007/s11071-019-05060-z ↗
30. Yilmaz E, Baysal V, Perc M, et al. Enhancement of pacemaker induced stochastic resonance by an autapse in a scale-free neuronal network. Sci China Tech Sci, 2016, 59: 364–37010.1007/s11431-015-5984-z ↗
31. Lin H, Wang C, Deng Q, et al. Review on chaotic dynamics of memristive neuron and neural network. Nonlinear Dyn, 2021, 106: 959–97310.1007/s11071-021-06853-x ↗
32. Yang R, Huang H M, Guo X. Memristive synapses and neurons for bioinspired computing. Adv Electron Mater, 2019, 5: 190028710.1002/aelm.201900287 ↗
33. Bao B C, Zhu Y X, Ma J, et al. Memristive neuron model with an adapting synapse and its hardware experiments. Sci China Tech Sci, 2021, 64: 1107–111710.1007/s11431-020-1730-0 ↗
34. Ignatov M, Ziegler M, Hansen M, et al. A memristive spiking neuron with firing rate coding. Front Neurosci, 2015, 9: 37610.3389/fnins.2015.00376 ↗
35. Kafraj M S, Parastesh F, Jafari S. Firing patterns of an improved Izhikevich neuron model under the effect of electromagnetic induction and noise. Chaos Solitons Fractals, 2020, 137: 10978210.1016/j.chaos.2020.109782 ↗
36. Lu L L, Jia Y, Xu Y, et al. Energy dependence on modes of electric activities of neuron driven by different external mixed signals under electromagnetic induction. Sci China Tech Sci, 2019, 62: 427–44010.1007/s11431-017-9217-x ↗
37. Yuan Z X, Feng P H, Du M M, et al. Dynamical response of a neuron-astrocyte coupling system under electromagnetic induction and external stimulation. Chin Phys B, 2020, 29: 03050410.1088/1674-1056/ab7441 ↗
38. Zhang Y, Xu Y, Yao Z, et al. A feasible neuron for estimating the magnetic field effect. Nonlinear Dyn, 2020, 102: 1849–186710.1007/s11071-020-05991-y ↗
39. Liu Y, Xu W, Ma J, et al. A new photosensitive neuron model and its dynamics. Front Inform Technol Electron Eng, 2020, 21: 1387–139610.1631/FITEE.1900606 ↗
40. Zhang X, Ma J. Wave filtering and firing modes in a light-sensitive neural circuit. J Zhejiang Univ Sci A, 2021, 22: 707–72010.1631/jzus.A2100323 ↗
41. Ma X W, Xu Y. Taming the hybrid synapse under energy balance between neurons. Chaos Solitons Fractals, 2022, 159: 11214910.1016/j.chaos.2022.112149 ↗
42. Wang C, Sun G, Yang F, et al. Capacitive coupling memristive systems for energy balance. AEU-Int J Electron Commun, 2022, 153: 15428010.1016/j.aeue.2022.154280 ↗
43. Zhou P, Zhang X, Ma J. How to wake up the electric synapse coupling between neurons? Nonlinear Dyn, 2022, 108: 1681–169510.1007/s11071-022-07282-0 ↗
44. Xie Y, Yao Z, Ma J. Phase synchronization and energy balance between neurons. Front Inform Technol Electron Eng, 2022, doi: https://doi.org/10.1631/FITEE.210056310.1631/FITEE.2100563 ↗
45. Kyprianidis I M, Papachristou V, Stouboulos I N, et al. Dynamics of coupled chaotic Bonhoeffer-van der Pol Oscillators. WSEAS Trans Syst, 2012, 11: 516–526
46. Zhou P, Yao Z, Ma J, et al. A piezoelectric sensing neuron and resonance synchronization between auditory neurons under stimulus. Chaos Solitons Fractals, 2021, 145: 11075110.1016/j.chaos.2021.110751 ↗
47. Guo Y, Zhu Z, Wang C, et al. Coupling synchronization between photoelectric neurons by using memristive synapse. Optik, 2020, 218: 16499310.1016/j.ijleo.2020.164993 ↗
48. Xie Y, Yao Z, Hu X, et al. Enhance sensitivity to illumination and synchronization in light-dependent neurons. Chin Phys B, 2021, 30: 12051010.1088/1674-1056/ac1fdc ↗
49. Xie Y, Zhu Z G, Zhang X F, et al. Control of firing mode in nonlinear neuron circuit driven by photocurrent. Acta Phys Sin, 2021, 70: 21050210.7498/aps.70.20210676 ↗
50. Zhou P, Hu X, Zhu Z, et al. What is the most suitable Lyapunov function? Chaos Solitons Fractals, 2021, 150: 11115410.1016/j.chaos.2021.111154 ↗
51. Wu F, Guo Y, Ma J. Reproduce the biophysical function of chemical synapse by using a memristive synapse. Nonlinear Dyn, 2022, 109: 2063–208410.1007/s11071-022-07533-0 ↗
52. Wang C, Lv M, Alsaedi A, et al. Synchronization stability and pattern selection in a memristive neuronal network. Chaos, 2017, 27: 11310810.1063/1.5004234 ↗
53. Rajagopal K, Jafari S, Karthikeyan A, et al. Effect of magnetic induction on the synchronizability of coupled neuron network. Chaos, 2021, 31: 08311510.1063/5.0061406 ↗
54. Ge M, Jia Y, Xu Y, et al. Wave propagation and synchronization induced by chemical autapse in chain Hindmarsh-Rose neural network. Appl Math Computat, 2019, 352: 136–14510.1016/j.amc.2019.01.059 ↗
55. Hussain I, Jafari S, Ghosh D, et al. Synchronization and chimeras in a network of photosensitive FitzHugh-Nagumo neurons. Nonlinear Dyn, 2021, 104: 2711–272110.1007/s11071-021-06427-x ↗
56. Hussain I, Ghosh D, Jafari S. Chimera states in a thermosensitive FitzHugh-Nagumo neuronal network. Appl Math Computat, 2021, 410: 12646110.1016/j.amc.2021.126461 ↗
57. Tah F A, Tabi C B, Kofane T C. Pattern formation in the Fitzhugh-Nagumo neuron with diffusion relaxation. Chaos Solitons Fractals, 2021, 147: 11097410.1016/j.chaos.2021.110974 ↗
58. Yao Z, Wang C. Control the collective behaviors in a functional neural network. Chaos Solitons Fractals, 2021, 152: 11136110.1016/j.chaos.2021.111361 ↗
59. Lu L L, Yi M, Liu X Q. Energy-efficient firing modes of chay neuron model in different bursting kinetics. Sci China Tech Sci, 2022, 65: 1661–167410.1007/s11431-021-2066-7 ↗
60. Yuan Y Y, Yang H, Han F, et al. Traveling chimera states in locally coupled memristive Hindmarsh-Rose neuronal networks and circuit simulation. Sci China Tech Sci, 2022, 65: 1445–145510.1007/s11431-021-2042-4 ↗
61. Bao H, Hua Z Y, Liu W B, et al. Discrete memristive neuron model and its interspike interval-encoded application in image encryption. Sci China Tech Sci, 2021, 64: 2281–229110.1007/s11431-021-1845-x ↗
62. Chen M, Qi J W, Wu H G, et al. Bifurcation analyses and hardware experiments for bursting dynamics in non-autonomous memristive FitzHugh-Nagumo circuit. Sci China Tech Sci, 2020, 63: 1035–104410.1007/s11431-019-1458-5 ↗
63. Bao H, Chen M, Wu H G, et al. Memristor initial-boosted coexisting plane bifurcations and its extreme multi-stability reconstitution in two-memristor-based dynamical system. Sci China Tech Sci, 2020, 63: 603–61310.1007/s11431-019-1450-6 ↗
64. Li Y Y, Gu H G, Jia B, et al. The nonlinear mechanism for the same responses of neuronal bursting to opposite self-feedback modulations of autapse. Sci China Tech Sci, 2021, 64: 1459–147110.1007/s11431-020-1753-y ↗
65. Ge M Y, Wang G W, Jia Y. Influence of the Gaussian colored noise and electromagnetic radiation on the propagation of subthreshold signals in feedforward neural networks. Sci China Tech Sci, 2021, 64: 847–85710.1007/s11431-020-1696-8 ↗
66. Wu S F, Wang L B, Zhao Y W, et al. Chronic electrical stimulation induces functional network changes in cortical neuron cultures. Sci China Tech Sci, 2020, 63: 637–64710.1007/s11431-019-1442-2 ↗