A Raspberry Pi Based Hardware Implementations of Various Neuron Models

Abstract

Abstract The implementation of biological neuron models plays an important role to understand brain functionality and robotic applications. Analog and digital methods are preferred during implementation processes. The Raspberry Pi (RPi) microcontroller/microprocessor has the potential to be a new platform that can easily solve complex mathematical operations, does not have memory limitations, which will take advantage while realizing biological neuron models. In this paper, Hodgkin-Huxley (HH), FitzHugh-Nagumo (FHN), Morris-Lecar (ML), Hindmarsh-Rose (HR), and Izhikevich (IZ) neuron models, which are the most popular in the literature, have been both implemented on a standard equipped RPi and simulated on MATLAB. For the numerical solution of each neuron model, the one-step method (4th Runge-Kutta (RK4), the new version of Runge-Kutta (RKN)), the multi-step method (Adams-Bashforth (AB), Adams-Moulton (AM)), and predictor-corrector method (Adams-Bashforth-Moulton (ABM)) are preferred to compare results. The implementation of HH, ML, FHN, HR, and IZ neuron models on RPi and the comparison of RK4, RKN, AB, AM and ABM numerical methods in the implementation of neuron models were made for the first time in this study. Firstly, MATLAB simulations of the various behaviours which belong to HH, ML, FHN, HR, and IZ neuron models were completed. Then those models were realized on RPi and the outputs of the models are experimentally produced. The error values between the simulation and implementation results were calculated and also presented in the tables. The experimental results show that RPi can be considered as a new tool to realize complex neuron models.