Neurobiological Foundations of AI: Tracing the Evolution of Neural Networks
Vishwas Gupta · 2025
This chapter explores the burgeoning intersection of artificial intelligence (AI) and neuroscience, highlighting the reciprocal relationship between these fields. It elucidates how AI draws inspiration from the brain’s structure, function, and plasticity to develop more biologically inspired algorithms and systems. Key topics include neural network architectures, such as feedforward, recurrent, and convolutional networks, and their applications in image recognition, speech processing, robotics, and autonomous systems. In addition, the discourse delves into ethical considerations, emphasizing privacy, fairness, accountability, and the responsible development of AI technology. Moreover, it elucidates the future directions of neurobiologically inspired AI, encompassing spiking neural networks, neuromorphic computing, brain–computer interfaces, and AI and neuroscience research integration. This convergence promises to unlock new frontiers in understanding the brain’s complexities while driving innovations in AI technology, with profound implications for healthcare, education, robotics, and beyond. This synergy can reshape our understanding of intelligence through interdisciplinary collaboration and ethical stewardship and revolutionize human-machine interaction in the digital age.