AI and Neuroscience Convergence: Emerging Models for Understanding Human Cognition and Intelligent Behaviour

Imran Sayeed

AI and Neuroscience Convergence: Emerging Models for Understanding Human Cognition and Intelligent Behaviour

Author(s)	Imran Sayeed
Country	United Kingdom
Abstract	The convergence of artificial intelligence and neuroscience is creating a rapidly expanding interdisciplinary research field focused on understanding intelligence, cognition, perception, learning, memory, decision-making, and adaptive behaviour. Neuroscience provides insights into the biological mechanisms underlying human cognition, while artificial intelligence offers computational models capable of representing, simulating, and testing hypotheses about intelligent behaviour. This paper examines emerging approaches at the intersection of AI and neuroscience, including neural computation, brain-inspired artificial neural networks, computational neuroscience, reinforcement learning, predictive processing, large-scale brain modelling, neural decoding, brain–computer interfaces, and neuro-symbolic intelligence. Particular attention is given to how neuroscience can inform the development of more adaptive, efficient, robust, and interpretable AI systems, while AI can provide new tools for analysing complex neural data and modelling cognitive processes. The paper proposes a conceptual AI–Neuroscience Convergence Framework integrating biological observation, computational modelling, machine learning, cognitive experimentation, and iterative validation. The paper also discusses major challenges involving neural complexity, data limitations, interpretability, biological plausibility, ethical concerns, privacy, and the difficulty of translating correlations between brain activity and behaviour into causal explanations. It argues that the future of intelligent systems may depend not on directly replicating the human brain but on selectively incorporating principles of biological intelligence into computational architectures. The convergence of these disciplines therefore has potential implications for cognitive science, healthcare, robotics, education, human–computer interaction, and the design of next-generation intelligent systems.
Keywords	Artificial Intelligence, Neuroscience, Human Cognition, Computational Neuroscience, Brain-Inspired AI, Neural Networks, Reinforcement Learning, Brain–Computer Interfaces, Cognitive Computing, Intelligent Behaviour.
Field	Engineering
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-10-22

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American Journal of Advanced Multidisciplinary Research and Innovation

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AI and Neuroscience Convergence: Emerging Models for Understanding Human Cognition and Intelligent Behaviour

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