在一个反复复的神经网络中,行为雪崩和内部神经元动态之间的非微不足道关系
Anja Rabus1, Maria Masoliver1,2, Aaron J Gruber3
1Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, Alberta T2N 1N4, Canada.
Chaos (Woodbury, N.Y.)
|May 8, 2024
概括
大脑活动和行为之间的关系是复杂的. 这项研究表明,虽然训练神经网络可以改变大脑内部动态,但关键的统计性质,如神经雪崩大小分布,保持不变,这表明存在一个非微不足道的联系.
科学领域:
- 计算神经科学是一种神经科学.
- 复杂的系统复杂的系统.
- 动态系统 动态系统
背景情况:
- 神经元活动和行为在一个闭环系统中相互连接.
- 以前的研究表明,无尺度神经元和行为动态之间存在直接关系.
- 这些研究往往侧重于本地网络动态,限制了全面的理解.
研究的目的:
- 在模型系统中研究内部神经元动态和输出统计数据之间的关系.
- 确定行为中的权力定律分布是否与特定的神经元动态相关.
- 为了探索神经元动力学在干扰下的强度.
主要方法:
- 利用在混乱状态中初始化的循环神经网络 (RNN).
- 训练RNN以产生具有权力定律持续时间分布的行为状态.
- 在训练和随机网络配置中分析了神经元雪崩的大小和持续时间分布.
主要成果:
- 训练改变了内部网络的动态,产生了权力规律的神经雪崩的大小分布.
- 随机化网络连接变化在很大程度上保留了神经元雪崩大小分布中的功率定律特征.
- 神经元雪崩的大小分布是不变的,即使行为力量定律动态没有被保留.
结论:
- 行为和神经元统计特征之间的直接一对一对应并未确立.
- 神经元动力学和行为之间的关系是非碎和复杂的.
- 固有的神经元动态的统计性质可以保留,尽管输出产生机制的干扰.
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