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Frontiers of Information Technology & Electronic Engineering >> 2020, Volume 21, Issue 9 doi: 10.1631/FITEE.1900606

A new photosensitive neuron model and its dynamics

Affiliation(s): School of Mathematics and Statistics, Yancheng Teachers University, Yancheng 224002, China; Department of Physics, Lanzhou University of Technology, Lanzhou 730050, China; School of Science, Chongqing University of Posts and Telecommunications, Chongqing 430065, China; NAAM-Research Group, Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia; less

Received: 2019-11-09 Accepted: 2020-09-09 Available online: 2020-09-09

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Biological neurons can receive inputs and capture a variety of external stimuli, which can be encoded and transmitted as different electric signals. Thus, the membrane potential is adjusted to activate the appropriate firing modes. Indeed, reliable s should take intrinsic biophysical effects and functional encoding into consideration. One fascinating and important question is the physical mechanism for the transcription of external signals. External signals can be transmitted as a transmembrane current or a signal voltage for generating action potentials. We present a model to estimate the nonlinear encoding and responses of neurons driven by external optical signals. In the model, a (phototube) is used to activate a simple FitzHugh-Nagumo (FHN) neuron, and then external optical signals (illumination) are imposed to excite the for generating a time-varying current/voltage source. The -coupled FHN neuron can therefore capture and encode external optical signals, similar to artificial eyes. We also present detailed analysis for estimating the mode transition and firing pattern selection of neuronal electrical activities. The sampled time series can reproduce the main characteristics of biological neurons (quiescent, spiking, , and even chaotic behaviors) by activating the in the neural circuit. These results could be helpful in giving possible guidance for studying neurodynamics and applying neural circuits to detect optical signals.

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