Enhancing Human-Machine Interaction: Leveraging Neuromorphic Chips for Adaptive Learning and Control in Neural Prosthetics and Artificial Intelligence
DOI:
https://doi.org/10.32628/CSEIT241061135Keywords:
Human-machine interaction, Neuromorphic Chips, Adaptive Learning, Control, Neural Prosthetics, Artificial intelligence, Brain-Computer Interfaces, Efficient Learning, Adaptability, and Parallel ProcessingAbstract
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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