通过分裂性正常化稳定循环神经网络.
Flaviano Morone1, Shivang Rawat2, David J Heeger3
1Center for Neural Science, NYU and Center for Soft Matter Research, Department of Physics, NYU.
bioRxiv : the preprint server for biology
|June 6, 2025
概括
循环神经网络可以通过分裂规范化来保持超越传统限制的稳定性. 这种神经机制通过抑制神经元反应来增强稳定性,为其在生物系统中的流行提供了一个潜在的原因.
科学领域:
- 神经科学是一个神经科学.
- 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:
- 由于对突触权重的敏感性,确保经常性神经网络的稳定性是具有挑战性的.
- 线性动态模型需要单位圆内的自值来保持稳定性,这是反复网络的严格条件.
研究的目的:
- 为了研究是否经常性神经网络可以实现稳定,即使光谱半径超过1.
- 探索分裂性正常化在维持神经电路稳定性的作用.
主要方法:
- 经常性神经网络动态的理论分析.
- 数字模拟用于验证理论预测.
- 分析预测正常化崩的情况.
主要成果:
- 具有分裂正常化的循环神经网络可以在光谱半径超过1时保持稳定.
- 关键减速,这是不稳定的早期预警信号,在稳定性丧失之前.
- 临界减速的开始与正常化的崩相关.
结论:
- 分割正常化对于增强循环神经网络的动态稳定性至关重要.
- 神经系统中正常化的普遍性可能源于它在稳定性方面的作用,而不仅仅是计算.
- 这些发现提供了对生物和人工神经电路设计原理的见解.
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