Brain-inspired models and their applications

Authors

DOI:

https://doi.org/10.55630/mem.2025.54.018-024

Keywords:

artificial intelligence, neural biology, neuromorphic computing

Abstract

Even though the contemporary artificial intelligence (AI) became a powerful tool in many areas and even in our everyday life, it still cannot outperform the human brain. The natural intelligence is a result of collaborative work of neural cell ensembles having much more elaborated functionality and connectivity than the artificial neural networks. The paper presents the basic working principles in the brain, neuromorphic computing inspired by them and their applications.

Author Biography

Petia Koprinkova-Hristova, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences

Institute of Information and Communication Technologies
Bulgarian Academy of Sciences
Acad. G. Bonchev Str., Bl. 2
1113 Sofia, Bulgaria

References

M. Barnell, C. Raymond, M. Wilson, D. Isereau, E. Cote, D. Brow., C. Cicotta. Demonstrating Advanced Machine Learning and Neuromorphic Computing Using IBM’s NS16e (2020) Advances in Intelligent Systems and Computing, 1228 AISC, pp. 1-11 DOI: 10.1007/978-3-030-52249-0_1

Z. Cai, X. Li. Neuromorphic Brain-Inspired Computing with Hybrid Neural Networks (2021) 2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021, art. no. 9456483, pp. 343-347 DOI: 10.1109/AIID51893.2021.9456483

L. Cheng, Y. Liu. Spiking neural networks: Model, learning algorithms and applications (2018) Kongzhi yu Juece/Control and Decision, 33 (5), pp. 923-937 DOI: 10.13195/j.kzyjc.2017.1444

A. Goriely. Eighty-six billion and counting: do we know the number of neurons in the human brain?, Brain, (2024) awae390, DOI: 10.1093/brain/awae390

Guetig et al. Learning input correlations through nonlinear temporally asymmetric hebbian plasticity (2003) Journal of Neuroscience 23, pp. 3697-3714 DOI: 10.1523/JNEUROSCI.23-09-03697.2003

E. M. Izhikevich. Simple Model of Spiking Neurons IEEE Transactions on Neural Networks (2003) 14, pp. 1569-1572

J. Liu, H. Wu. Research hotspots and trends of brain-inspired intelligence (2021) Chinese Journal of Biomedical Engineering, 40 (1), pp. 91-98

Y. Lv, H. Chen, Q. Wang, X. Li, C. Xie, Z. Song. Post-silicon nano-electronic device and its application in brain-inspired chips (2022) Frontiers in Neurorobotics, 16, art. no. 948386 DOI: 10.3389/fnbot.2022.948386

K. Roy, A. Jaiswal, P. Panda. Towards spike-based machine intelligence with neuromorphic computing (2019) Nature, 575 (7784), pp. 607-617 DOI: 10.1038/s41586-019-1677-2

R. S. Sutton, A. G. Barto. Reinforcement Learning: An Introduction, Second Edition, MIT Press, Cambridge, MA (2018)

R.-D. Wang, R. Wang, T.-D. Zhang, S. Wang. A Survey of Research on Robotic Brain-inspired Intelligence (2024) Zidonghua Xuebao/Acta Automatica Sinica, 50 (8), pp. 1485-1501 DOI: 10.16383/j.aas.c230705

Y. Wang, J. Lu, M. Gavrilova, R. A. Fiorini, J. Kacprzyk. Brain-Inspired Systems (BIS): Cognitive Foundations and Applications (2018) Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, art. no. 8616173, pp. 995-1000 DOI: 10.1109/SMC.2018.00177

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Published

2025-03-27

How to Cite

[1]
Koprinkova-Hristova, P. 2025. Brain-inspired models and their applications. Mathematics and Education in Mathematics. 54, (Mar. 2025), 018–024. DOI:https://doi.org/10.55630/mem.2025.54.018-024.