神经元的多样性可以改善机器学习,用于物理学和超越它
Anshul Choudhary1,2, Anil Radhakrishnan3, John F Lindner4,5
1Nonlinear Artificial Intelligence Laboratory, Physics Department, North Carolina State University, Raleigh, NC, 27607, USA. anshul.choudhary@jax.org.
Scientific reports
|August 26, 2023
概括
具有多样化,自我学习的神经元的人工神经网络 (ANN) 优于同质的神经元. 这种方法可以提高图像分类和非线性回归任务的性能,因为它使神经元能够调整它们的激活功能.
科学领域:
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
- 动态系统 动态系统
背景情况:
- 传统的人工神经网络 (ANN) 经常使用同质的神经元,限制了它们的适应性.
- 大自然表明,多样性是有利的,但这一原则在ANN中未得到充分探索.
研究的目的:
- 研究神经元多样性对ANN性能的影响.
- 开发由神经元组成的ANN,这些神经元学习自己的激活功能.
主要方法:
- 构建的神经网络,在这个神经网络中,个别的神经元会超学习它们的激活功能.
- 雇佣子网络来实例化这些适应性神经元.
- 在图像分类,非线性回归和基于物理的任务上进行了测试.
主要成果:
- 神经元迅速多样化了它们的学习激活功能.
- 不同的神经网络的表现明显优于同质的神经网络.
- 成功应用于包括数字分类,预测和学习物理系统动态等任务.
结论:
- 学习的神经元多样性增强了人工神经网络的能力.
- 这种方法为通过模仿自然系统来优化ANN提供了新的视角.
- 突出了自然和人工系统中多样性选择的原则.
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