This paper explores brain-inspired computing as a transformative approach that integrates principles from neurobiology with artificial intelligence (AI) to enhance computational efficiency and adaptability. By mimicking neural structures and cognitive processes, brain-inspired models aim to overcome limitations of traditional AI systems, enabling more robust learning, pattern recognition, and decision-making. The study reviews key neurobiological mechanisms, such as neural plasticity and parallel processing, and discusses their applications in neuromorphic hardware and advanced AI algorithms. This interdisciplinary convergence offers promising pathways for developing intelligent systems that closely emulate human brain function.

Brain-inspired computing, neurobiology, artificial intelligence

Schuman, C. D. (2017). The State of Neuromorphic Computing: A Survey of the Current Landscape. IEEE Transactions on Neural Networks and Learning Systems, 28(11), 2947-2961. DOI: https://www.doi.org/10.48550/arXiv.1705.06963

Furber, S. (2016). Large-Scale Brain Simulation: The SpiNNaker Project. Proceedings of the IEEE, 104(1), 152-163. DOI: https://www.doi.org/10.1109/JPROC.2014.2304638

Fig. 1. Prasanna Date: Opportunities for neuromorphic computing algorithms and applications. Research gate. DOI:10.1038/s43588-021-00184-y.

Fig. 2. Yoeri Van de Burgt: Organic materials and devices for brain-inspired computing: From artificial implementation to biophysical realism. Research gate. DOI: 10.1557/mrs.2020.194.

Fig. 3. T. Nathan Mundhenk, TrueNorth Ecosystem for Brain-Inspired Computing: Scalable Systems, Software, and Applications. Research Gate. DOI: 10.1109/SC.2016.11.

Wikichip: Loihi-Intel, https://en.wikichip.org/wiki/intel/loihi.

Hasan Erdem Yantır, Towards Efficient Neuromorphic Hardware: Unsupervised Adaptive Neuron Pruning. Research Gate. DOI: 10.3390/electronics9071059.

Sheikh, Z., & Khetade, V. (2019). Modeling and Simulation of Asynchrony in Neuromorphic Computing. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 9, pp. 676–685). https://doi.org/10.35940/ijitee.i7747.078919

Magapu, H., Krishna Sai, M. R., & Goteti, B. (2024). Human Deep Neural Networks with Artificial Intelligence and Mathematical Formulas. In International Journal of Emerging Science and Engineering (Vol. 12, Issue 4, pp. 1–2). https://doi.org/10.35940/ijese.c9803.12040324

Mukherjee, P., Palan, P., & Bonde, M. V. (2021). Using Machine Learning and Artificial Intelligence Principles to Implement a Wealth Management System. In International Journal of Soft Computing and Engineering (Vol. 10, Issue 5, pp. 26–31). https://doi.org/10.35940/ijsce.f3500.0510521

Priyatharshini, Dr. R., Ram. A.S, A., Sundar, R. S., & Nirmal, G. N. (2019). Real-Time Object Recognition using Region based Convolution Neural Network and Recursive Neural Network. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 4, pp. 2813–2818). https://doi.org/10.35940/ijrte.d8326.118419

Anilkumar B, P.Rajesh Kumar, Classification of MR Brain tumors with Deep Plain and Residual Feed forward CNNs through Transfer learning. (2019). In International Journal of Engineering and Advanced Technology (Vol. 8, Issue 6, pp. 1758–1763). https://doi.org/10.35940/ijeat.f8437.088619