基于神经SDE的噪音神经元的峰值控制
Fumiya Sato1, Masaki Ogura2,3, Airi Sashie4
1School of Engineering Science, Osaka University, Osaka, Japan.
PloS one
|September 16, 2025
概括
这项研究引入了一种新方法,即使用神经静态微分方程 (Neural Stochastic Differential Equations,神经SDE) 来精确控制神经尖峰的时间,即使在噪音条件下也是如此. 该技术适应个体神经元的行为,以有效治疗神经系统疾病.
科学领域:
- 计算神经科学是一种神经科学.
- 神经工程是神经工程.
- 生物物理学的生物物理.
背景情况:
- 控制个体神经元的峰值时间对于治疗神经系统疾病至关重要.
- 现实世界的神经控制应用需要对生物噪声和各种神经元发射模式强大的方法.
- 现有的模型经常简化神经动力学或忽略环境噪音.
研究的目的:
- 开发一种使用神经静态微分方程 (Neural Stochastic Differential Equations,神经SDE) 来精确控制神经元中尖峰时间的新方法.
- 确保该方法在不同类型的神经元和不同噪音条件下有效.
- 为了使神经元特定的控制信号能够适应单个发射特征.
主要方法:
- 利用伊希克维奇模型捕捉各种神经元发射行为.
- 使用神经静态微分方程 (神经SDE) 来控制尖峰的时间.
- 通过随机梯度下降和反向传播实现外部电流的代训练,以最大限度地减少发射和定时错误.
主要成果:
- 在常规尖端,爆发和快速尖端神经元模型中实现了精确的尖端定时控制.
- 即使在显著的噪音干扰下,也证明了强度和有效性.
- 在控制早期尖端事件方面观察到特别高的精度.
结论:
- 拟议的神经SDE框架为神经尖峰时间控制提供了一个强大的和可泛化的方法.
- 该方法直接考虑了生物噪声和复杂的内在动态,优于传统方法.
- 结果表明,它适用于真实世界的应用,如神经假肢和自适应性闭环治疗系统.
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