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Taming the hybrid synapse under energy balance between neurons

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  • Ma, Xiaowen
  • Xu, Ying

Abstract

Biological neurons can present a variety of firing modes and adaptive regulation in the synapses is effective to encode information and propagate electric signals to other neurons. From a dynamical viewpoint, neurons and even chaotic oscillators can be regulated to reach synchronization by setting appropriate coupling intensities. From the physical aspect, these nonlinear oscillators are controlled to reach energy balance accompanied by synchronization. A quiescent neuron still contains static electric field energy and the occurrence of firing patterns (spiking, bursting, and even chaotic mode) can activate magnetic field energy because of the propagation of intracellular Calcium, sodium, potassium in neurons and channel currents. Gap junction coupling accounts for the transient electric coupling between neurons, and the chemical synapse coupling results from the magnetic field coupling when the neurotransmitter is released from the presynaptic terminal to the postsynaptic terminal. In the same functional region, the synaptic coupling intensity could be time-varying and thus neurons can be switched from different synchronous states. In this work, a hybrid synapse composed of resistor and induction coil is used to control the synchronous firing modes in neurons, which are developed from a simple neural circuit composed of one capacitor, induction coil, nonlinear resistor, external voltage source, and the coupling gain in the hybrid synapse is dependent on the energy diversity between two neurons. An adaptive law is suggested to adjust the field coupling intensity and it is confirmed that two neurons are synchronized complete until they are kept energy balanced. These results provide possible guidance to know the growth and creation mechanism for synaptic connections to neurons.

Suggested Citation

  • Ma, Xiaowen & Xu, Ying, 2022. "Taming the hybrid synapse under energy balance between neurons," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    • Handle: RePEc:eee:chsofr:v:159:y:2022:i:c:s0960077922003599
    DOI: 10.1016/j.chaos.2022.112149
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    References listed on IDEAS

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    1. Cofré, Rodrigo & Cessac, Bruno, 2013. "Dynamics and spike trains statistics in conductance-based integrate-and-fire neural networks with chemical and electric synapses," Chaos, Solitons & Fractals, Elsevier, vol. 50(C), pages 13-31.
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    5. Zhou, Ping & Hu, Xikui & Zhu, Zhigang & Ma, Jun, 2021. "What is the most suitable Lyapunov function?," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    6. Xu, Ying & Guo, Yeye & Ren, Guodong & Ma, Jun, 2020. "Dynamics and stochastic resonance in a thermosensitive neuron," Applied Mathematics and Computation, Elsevier, vol. 385(C).
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    Cited by:

    1. Guo, Yitong & Xie, Ying & Ma, Jun, 2023. "Nonlinear responses in a neural network under spatial electromagnetic radiation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
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    3. Jia, Junen & Wang, Chunni & Zhang, Xiaofeng & Zhu, Zhigang, 2024. "Energy and self-adaption in a memristive map neuron," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).

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