Artificial Electrical Morris–Lecar Neuron

Abstract

In this paper, an experimental electronic neuron based on a complete Morris-Lecar model is presented, which is able to become an experimental unit tool to study collective association of coupled neurons. The circuit design is given according to the ionic currents of this model. The experimental results are compared with the theoretical prediction, leading to a good agreement between them, which therefore validate the circuit. The use of some parts of the circuit is also possible for other neurons models, namely for those based on ionic currents.

Smooth nonlinear fitting scheme for analog multiplierless implementation of Hindmarsh–Rose neuron model

“…Certainly, the presented smooth nonlinear fitting scheme can also be applied in other neuron models or neural networks, such as the Morris-Lecar neuron model [25] and coupled Hindmarsh-Rose neuron model [29], which deserve our future study.…”

“…Based on the off-the-shelf analog components and on-chip digital devices, numerous analog and digital neuromorphic circuits were developed to simulate standalone neurons and coupled neural networks [22,23]. Due to the power-efficiency and compactness of analog circuits, mathematical neuron models were widely implemented in analog [24][25][26].…”

“…Based on numerous off-the-shelf discrete components, Bao et al optimized an analog circuit design to implement an adapting synapse-based neuron model [24], Behdad et al presented an experimental electronic neuron to achieve a complete Morris-Lecar model [25], and Arthur and Boahen developed a silicon-based integrate-and-fire neuron using a dynamical system approach [26].…”

“…These electronic neurons with analog and digital circuit implementations are capable to reproduce lots of different neuron dynamics and spiking/bursting behaviors that might appear in biological neurons [25,31]. Encouraged by the above piecewise liner approximation approaches, this paper presents a novel smooth nonlinear fitting scheme to implement an analog multiplierless circuit for the 2D/3D HR neuron model.…”

A Raspberry Pi Based Hardware Implementations of Various Neuron Models

“…In literature, although there are several implementations of neuron models on various platforms such as discrete elements, VLSI, FPAA, and FPGA as mentioned above, no study has been encountered using RPi. In other studies, for numerical solutions of neuron models, the RK4 method has been generally preferred, because it is easier to simulate than the other methods (Alteriis et al 2021;Behdad et al 2015;De Alteriis and Oddo 2021;Momani et al 2014;Rostami et al 2018;Skocik and Long 2014;Tuckwell and Jost 2009;Valadez-Godínez et al 2020;Xu et al 2018;Zhang et al 2011). A few studies about RKN, AB, AM, and ABM methods have been presented (Ramadoss et al 2021;Sweilam and Assiri 2016;V.…”