Open AccessGeneration of Neuromorphic Oscillators via Second-order Memristive Circuits with Modified Chua Corsage Memristor
Author(s) -
Zhenyu Song,
Yue Liu
Publication year - 2023
Publication title -
ieee access
Language(s) - English
Resource type - Journals
ISSN - 2169-3536
DOI - 10.1109/access.2023.3318117
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The study on neuromorphic oscillation behaviors and their oscillators have been considered as one of the most straightforward approaches to mimic some biological neurons via the configured nonlinear memristive equivalent circuits. Also, neuromorphic oscillation is one classical oscillation phenomenon, both the frequency response and zero-pole analysis could be clearly regarded as a complete description of the sinusoidal oscillation behavior for a nonlinear circuit. As one of the most classical memristors, Chua corsage memristor (CCM) is so famous to exhibit chaotic oscillation and neuromorphic dynamics due to its locally active and edge of chaos. However, the parasitic phenomena in the practical circuits are unique and tiny but existed as one of the inherent characteristics, which could lead to some unexpected results. Even a pretty small perturbation may heavily affect the quality of the entire system. In this paper, one modified CCM with the parasitic parameter (named gp) is proposed. Then, some unique and unusual phenomena are captured and analyzed. Moreover, the analysis on the distributions for the locally-active domains (LADs) and edge of chaos are presented. Furthermore, one small-signal equivalent circuit with a positive capacitance is introduced, as well as its impedance and admittance functions. Also, both types of neuromorphic oscillators are captured and observed via external inductor and capacitor, respectively. Finally, the applications in neural networks are explored, which herald the proposed model could be more suitable to transmit the mental, physical fatigue, memory load and closer to simulating the actual neuromorphic systems.