Energy encoding in a biophysical neuron and adaptive energy balance under field coupling

Guoping Sun, Feifei Yang, Guodong Ren and Chunni Wang

Chaos, Solitons & Fractals, 2023, vol. 169, issue C

Abstract: Some nonlinear circuits can present similar dynamical properties in the outputs as biological neurons by taming the external stimulus and bifurcation parameter, and they can be used for further investigation on neural activities. In this work, a photoelectric converter such as phototube is incorporated into the Fitzhugh-Nagumo neural circuit and a light-sensitive neuron is obtained to describe the nonlinear response in visual neuron. Furthermore, an additive induction coil is also connected to the phototube in this branch circuit of this neural circuit for detecting the disturbance from external magnetic field, and this functional neuron can perceive external illumination and changes of external magnetic field synchronously. Scale transformation is applied for the circuit equation and equivalent photoelectric neuron model is obtained. The intrinsic field energy of the neural circuit is described by equivalent Hamilton energy and the dependence firing modes on energy is clarified. When neurons are clustered closely, energy diversity can trigger field coupling and the synaptic connection will be regulated in the coupling intensity adaptively. An artificial hybrid is developed by combining capacitor and inductor in the coupling channel, and synchronization approach and energy balance is controlled completely. These results provide potential explanation for knowing the growth mechanism for synapse connection and external energy injection can be effective to control the collective behaviors of neurons.

Keywords: Neuron model; Synapse; Field coupling; Hamilton energy; Synchronization (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:169:y:2023:i:c:s0960077923001315

DOI: 10.1016/j.chaos.2023.113230

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