神经重塑:人类大脑和人工智能在学习过程中的可塑性
Seyed-Ali Sadegh-Zadeh1, Mahboobe Bahrami2, Ommolbanin Soleimani3
1Department of Computing, School of Digital, Technologies and Arts, Staffordshire University Stoke-on-Trent ST4 2DE, UK.
American journal of neurodegenerative disease
|January 24, 2025
概括
人类和人工智能都通过代学习和连接调整展现神经重塑. 了解这些共同的原则可以推进人工智能设计和认知神经科学.
科学领域:
- 认知神经科学 认知神经科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 神经重塑是生物和人工系统的一个基本过程.
- 人类大脑的可塑性和人工神经网络 (ANN) 既适应又学习.
研究的目的:
- 研究人类大脑可塑性和ANN可塑性之间的相似之处和区别.
- 专注于这些系统中的学习过程.
主要方法:
- 使用文献评论进行比较分析.
- 用多层感知神经网络进行机器学习实验.
- 检查回归和分类问题.
主要成果:
- 机器学习模型通过代学习和优化来提高性能.
- 在加强和调整神经连接方面观察到并行,反映神经可塑性.
- 在适应性学习中展示了共享的原则.
结论:
- 了解神经和人工可塑性的共同原则和局限性是关键.
- 可以推动人工智能设计和认知神经科学的进步.
- 为未来的跨学科创新铺平了道路.
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