Neuro-Computational Synergy: Bridging Artificial Intelligence and Neuroscience for Next-Generation Cognitive Decision Systems

Abstract

The convergence of Artificial Intelligence (AI), neuroscience, and cognitive science has catalyzed a paradigm shift in understanding and engineering decision making systems. This comprehensive review examines the bidirectional relationship between brain-inspired computational models and neuroscientific insights, focusing on how reinforcement learning, prefrontal cortex mechanisms, and basal ganglia circuits inform the development of advanced cognitive architectures. We synthesize findings from 218 peer-reviewed studies spanning computational neuroscience, cognitive modeling, and AI applications, revealing that brain-inspired approaches not only enhance AI performance in complex decision tasks but also provide testable hypotheses about neural computation. Key themes include dopamine-modulated learning, meta-reinforcement learning in prefrontal networks, hierarchical cognitive control, and the integration of model-based and model-free decision strategies. We identify critical gaps in current understanding, including the challenge of 
scaling biologically plausible models to real-world applications and the need for unified frameworks that bridge multiple levels of analysis. This review demonstrates that neuro-computational synergy offers transformative potential for developing adaptive, context-sensitive AI systems while simultaneously advancing our understanding of human cognition. We conclude by outlining future research directions that emphasize cross-disciplinary collaboration, neuromorphic computing, and the ethical implications of brain-inspired AI systems.