DEVELOPING A SYSTEM FOR NEURAL PROTOTYPING OF NEURAL PROSTHESES BASED ON THE HYBRID SOFTWARE AND HARDWARE IMPLEMENTATION OF SPIKING NEURAL NETWORKS
https://doi.org/10.35266/1999-7604-2023-4-4
Abstract
The study presents a development of a modular system for prototyping a neural prosthesis aimed at compensating functions of damaged or lost structures of central nervous system using electronic devices that mimic the behavior of biological neurons. Artificial neurons demonstrate the ability to respond to an external stimulation or a signal from presynaptic neuron with impulsion; the ability to perform spatial and temporal summation, neural plasticity, all of which demonstrate capabilities of the developed system.
About the Author
S. V. Kravchenko
Krasnodar Branch of the S. Fedorov Eye Microsurgery Federal State Institution;
Kuban State Technological University, Krasnodar,
Russian Federation
Russian Federation
Candidate of Sciences (Medicine)
References
1. Кравченко С. В., Каде А. Х., Трофименко А. И. и др. Когнитивное нейропротезирование – путь от эксперимента к клиническому применению // Инновационная медицина Кубани. 2021. № 3. С. 64–72.
2. Тихомиров Г. В., Григорьева В. Н. Зрительная агнозия в клинике острого ишемического инсульта: частота встречаемости и связь с другими когнитивными нарушениями // Доктор.Ру. 2022. Т. 21, № 8. С. 12–17.
3. Berger T. W., Song D., Chan R. H. M. et al. A hippocampal cognitive prosthesis: Multi-input, multi-output nonlinear modeling and VLSI implementation. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2012;20(2):198–211.
4. Hampson R. E., Song D., Robinson B. S. et al. Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall. J Neural Eng. 2018;15(3):036014.
5. Xu T., Xiao N., Zhai X. et al. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning. J Neural Eng. 2018;15(1):016021.
6. Mikhaylov A., Pimashkin A., Pigareva Y. et al. Neurohybrid memristive CMOS-integrated systems for biosensors and neuroprosthetics. Front Neurosci. 2020;14:358.
7. Анохин К. В., Бурцев М. С., Ильин В. А. и др. Современные подходы к моделированию активности культур нейронов in vitro // Математическая биология и биоинформатика. 2012. Т. 7, № 2. С. 372–397.
8. Wang Z., Joshi S., Savel’ev S. et al. Fully memristive neural networks for pattern classification with unsu-pervised learning. Nat Electron. 2018;(1):137–145.
9. Sumner R. L., Spriggs M. J., Muthukumaraswamy S. D. et al. The role of Hebbian learning in human perception: A methodological and theoretical review of the human Visual Long-Term Potentiation paradigm. Neurosci Biobehav Rev. 2020;115:220‒237.
10. Petto A., Fredin Z., Burdo J. The use of modular, electronic neuron simulators for neural circuit construc-tion produces learning gains in an undergraduate anatomy and physiology course. J Undergrad Neurosci Educ. 2017;15(2):A151‒A156.
Review
For citations:
Kravchenko S.V. DEVELOPING A SYSTEM FOR NEURAL PROTOTYPING OF NEURAL PROSTHESES BASED ON THE HYBRID SOFTWARE AND HARDWARE IMPLEMENTATION OF SPIKING NEURAL NETWORKS. Proceedings in Cybernetics. 2023;22(4):26-32. (In Russ.) https://doi.org/10.35266/1999-7604-2023-4-4
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