人脑启发的人工智能神经网络。

IF 2.7 4区 医学 Q3 NEUROSCIENCES
Paschalis Theotokis
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引用次数: 0

摘要

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。本文研究了关键大脑区域(如脑干、感觉皮层、基底神经节、丘脑、边缘系统和前额叶皮层)与人工智能范式(包括通用人工智能、机器学习、深度学习和通用人工智能(AGI))之间的一致性。通过映射这些神经和计算架构,我在这里强调人工智能模型如何逐步模仿大脑的复杂性,从基本的模式识别和关联到高级推理。当前的挑战,如克服学习限制和实现类似的神经可塑性,与新兴的创新如神经形态计算一起解决。鉴于近年来人工智能的快速发展,这项工作强调了随着技术的指数级发展,不断重新评估我们的理解的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Human Brain Inspired Artificial Intelligence Neural Networks.

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.

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来源期刊
CiteScore
2.80
自引率
5.60%
发文量
173
审稿时长
2 months
期刊介绍: JIN is an international peer-reviewed, open access journal. JIN publishes leading-edge research at the interface of theoretical and experimental neuroscience, focusing across hierarchical levels of brain organization to better understand how diverse functions are integrated. We encourage submissions from scientists of all specialties that relate to brain functioning.
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