通过异突突触可塑性的神经网络中的进化学习
Zedong Bi1, Ruiqi Fu2, Guozhang Chen3
1Lingang Laboratory, Shanghai 200031, China.
iScience
|April 28, 2025
概括
本研究介绍了具有异质突触可塑性的进化算法 (EAs),作为一种新的,无梯度的方法,用于训练复杂的生物物理神经元模型. 这种方法模仿了大脑的可塑性,为传统训练技术提供了强大的替代方案.
科学领域:
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 训练复杂的生物物理神经元模型对于理解大脑功能至关重要.
- 传统的基于梯度的方法,如反向传播,在这些模型中与不稳定性和梯度问题作斗争.
研究的目的:
- 探索进化算法 (EAs) 与异质突触可塑性相结合,作为一种无梯度的训练替代方案.
- 开发一种以生物机制为灵感的模型来训练神经网络.
主要方法:
- 利用进化算法 (EA),重点关注异质突触可塑性.
- 具有独特的神经元信息路径的模拟剂,采用交替关门和多巴胺驱动的可塑性.
- 结合了生物机制,如多巴胺功能,树突脊柱的元可塑性,记忆重复和合作性突触可塑性.
主要成果:
- 用拟议的方法训练的神经网络在认知任务中表现出类似大脑的动态.
- 该方法成功地在前和反复架构中训练了尖端和模拟神经网络.
- 在MNIST分类和Atari游戏等任务上实现了与渐变式方法相比较的性能.
结论:
- 这项研究为训练生物物理神经元模型提供了一个强大的,无梯度的替代方案.
- 开发的方法扩展了培训方法,为推进计算神经科学和人工智能提供了重大潜力.
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