A Raspberry Pi-based Hardware Implementation of Various Neuron Models
The implementation of biological neuron models plays an important role in understanding the functionality of the brain. Generally, analog and digital methods are preferred during implementation processes. The Raspberry Pi (RPi) microcontroller has the potential to be a new platform that can easily s...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Kaunas University of Technology
2024-12-01
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| Series: | Elektronika ir Elektrotechnika |
| Subjects: | |
| Online Access: | https://eejournal.ktu.lt/index.php/elt/article/view/38201 |
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| Summary: | The implementation of biological neuron models plays an important role in understanding the functionality of the brain. Generally, analog and digital methods are preferred during implementation processes. The Raspberry Pi (RPi) microcontroller has the potential to be a new platform that can easily solve complex mathematical operations and 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 have been implemented on a standard-equipped RPi. For the numerical solution of each neuron model, the one-step method (4th order 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 numerical models RK4, RKN, AB, AM, and ABM in the implementation of neuron models were made for the first time in this study. Firstly, MATLAB simulations of the various behaviors belonging to the 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 errors are also presented in the tables. The results show that RPi can be considered as a new alternative tool for making complex neuron models. |
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| ISSN: | 1392-1215 2029-5731 |