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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| Format: | Article |
| Language: | English |
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Kaunas University of Technology
2024-12-01
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| Series: | Elektronika ir Elektrotechnika |
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| Online Access: | https://eejournal.ktu.lt/index.php/elt/article/view/38201 |
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| author | Vedat Burak Yucedag Ilker Dalkiran |
| author_facet | Vedat Burak Yucedag Ilker Dalkiran |
| author_sort | Vedat Burak Yucedag |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c2bf7b4222174264ad3f9842d6fe379e |
| institution | Kabale University |
| issn | 1392-1215 2029-5731 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Kaunas University of Technology |
| record_format | Article |
| series | Elektronika ir Elektrotechnika |
| spelling | doaj-art-c2bf7b4222174264ad3f9842d6fe379e2025-01-07T13:37:57ZengKaunas University of TechnologyElektronika ir Elektrotechnika1392-12152029-57312024-12-01306192810.5755/j02.eie.3820143455A Raspberry Pi-based Hardware Implementation of Various Neuron ModelsVedat Burak Yucedag0Ilker Dalkiran1https://orcid.org/0000-0003-2448-3556Graduate School of Natural and Applied Sciences, Electrical and Electronics Engineering, Erciyes University, Kayseri, TurkiyeGraduate School of Natural and Applied Sciences, Electrical and Electronics Engineering, Erciyes University, Kayseri, TurkiyeThe 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.https://eejournal.ktu.lt/index.php/elt/article/view/38201raspberry pihodgkin-huxleyhindmarsh-roseizhikevichrunge-kuttaadams-bashforth-moulton |
| spellingShingle | Vedat Burak Yucedag Ilker Dalkiran A Raspberry Pi-based Hardware Implementation of Various Neuron Models Elektronika ir Elektrotechnika raspberry pi hodgkin-huxley hindmarsh-rose izhikevich runge-kutta adams-bashforth-moulton |
| title | A Raspberry Pi-based Hardware Implementation of Various Neuron Models |
| title_full | A Raspberry Pi-based Hardware Implementation of Various Neuron Models |
| title_fullStr | A Raspberry Pi-based Hardware Implementation of Various Neuron Models |
| title_full_unstemmed | A Raspberry Pi-based Hardware Implementation of Various Neuron Models |
| title_short | A Raspberry Pi-based Hardware Implementation of Various Neuron Models |
| title_sort | raspberry pi based hardware implementation of various neuron models |
| topic | raspberry pi hodgkin-huxley hindmarsh-rose izhikevich runge-kutta adams-bashforth-moulton |
| url | https://eejournal.ktu.lt/index.php/elt/article/view/38201 |
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