Theotokis Paschalis
Second Department of Neurology, AHEPA General Hospital, Aristotle University of Thessaloniki, 54634 Thessaloniki, Greece.
Department of Histology-Embryology, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
J Integr Neurosci. 2025 Mar 28;24(4):26684. doi: 10.31083/JIN26684.
It is becoming increasingly evident that Artificial intelligence (AI) development draws inspiration from the architecture and functions of the human brain. This manuscript examines the alignment between key brain regions-such as the brainstem, sensory cortices, basal ganglia, thalamus, limbic system, and prefrontal cortex-and AI paradigms, including generic AI, machine learning, deep learning, and artificial general intelligence (AGI). By mapping these neural and computational architectures, I herein highlight how AI models progressively mimic the brain's complexity, from basic pattern recognition and association to advanced reasoning. Current challenges, such as overcoming learning limitations and achieving comparable neuroplasticity, are addressed alongside emerging innovations like neuromorphic computing. Given the rapid pace of AI advancements in recent years, this work underscores the importance of continuously reassessing our understanding as technology evolves exponentially.
越来越明显的是，人工智能（AI）的发展从人类大脑的结构和功能中汲取灵感。本文探讨了关键脑区（如脑干、感觉皮层、基底神经节、丘脑、边缘系统和前额叶皮层）与AI范式（包括通用AI、机器学习、深度学习和通用人工智能（AGI））之间的一致性。通过绘制这些神经和计算架构，我在此强调了AI模型如何从基本的模式识别和关联到高级推理逐步模仿大脑的复杂性。当前的挑战，如克服学习限制和实现可比的神经可塑性，与神经形态计算等新兴创新一同得到解决。鉴于近年来AI进步的快速步伐，随着技术呈指数级发展，这项工作强调了不断重新评估我们理解的重要性。
