Enhancing Human-Machine Interaction: Leveraging Neuromorphic Chips for Adaptive Learning and Control in Neural Prosthetics and Artificial Intelligence

Satnam Singh; Ishita Sabharwal; Shweta Kushwaha; Dr. Shilpi Jain; Dr. Madhur Jain

doi:10.32628/CSEIT241061135

Enhancing Human-Machine Interaction: Leveraging Neuromorphic Chips for Adaptive Learning and Control in Neural Prosthetics and Artificial Intelligence

Authors

Satnam Singh Student, Information Technology BPIT (GGSIPU), Delhi, India Author
Ishita Sabharwal Student, Information Technology BPIT (GGSIPU), Delhi, India Author
Shweta Kushwaha Student, Information Technology BPIT (GGSIPU), Delhi, India Author
Dr. Shilpi Jain Assistant Professor, Department of Maths, ARSD (DU), Delhi, India Author
Dr. Madhur Jain Assistant Professor, Department of IT, BPIT (GGSIPU), Delhi, India Author

DOI:

https://doi.org/10.32628/CSEIT241061135

Keywords:

Human-machine interaction, Neuromorphic Chips, Adaptive Learning, Control, Neural Prosthetics, Artificial intelligence, Brain-Computer Interfaces, Efficient Learning, Adaptability, and Parallel Processing

Abstract

This paper examines the integration of neuromorphic chips, AI, and neural prostheses to enhance human-machine interaction. Neuromorphic chips, modelled after the brain's neural architecture, enable efficient learning, adaptive behaviour, and energy-efficient processing in AI systems and prostheses. These chips improve pattern recognition, adaptive control, and integration with the human nervous system. In neural prostheses, they promise seamless brain-computer interfaces (BCI) to restore mobility for paralyzed individuals and enable precise control of devices for people with severe disabilities. For AI systems, neuromorphic chips support rapid learning from large datasets, enabling adaptability in dynamic environments and real-time decision-making.

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References

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Published

28-11-2024

Issue

Vol. 10 No. 6 (2024): November-December

Section

Research Articles

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]

Satnam Singh, Ishita Sabharwal, Shweta Kushwaha, Dr. Shilpi Jain, and Dr. Madhur Jain, “Enhancing Human-Machine Interaction: Leveraging Neuromorphic Chips for Adaptive Learning and Control in Neural Prosthetics and Artificial Intelligence”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 10, no. 6, pp. 933–940, Nov. 2024, doi: 10.32628/CSEIT241061135.