对于简单的网络和具有逻辑正常活动的神经元来说,明确的相互信息
Maurycy Chwiłka1, Jan Karbowski1
1Department of Mathematics, Informatics, and Mechanics, Institute of Applied Mathematics and Mechanics, University of Warsaw, Ulica Banacha 2, 02-097 Warsaw, Poland.
Physical review. E
|February 17, 2024
概括
具有重尾日志正常分布的网络,在自然界中很常见,被分析用于信息处理. 神经网络中的重尾增加了相互信息,这表明大脑可能会发展出异质动态来更好地处理信息.
科学领域:
- 计算神经科学是一种计算神经科学.
- 信息理论是信息理论.
- 统计物理学的统计物理.
背景情况:
- 许多自然网络都表现出重尾逻辑正常分布.
- 了解这些复杂系统中的信息流是至关重要的.
研究的目的:
- 导出对具有逻辑正常分布的随机变量网络的精确信息理论特征.
- 分析神经网络中的相互信息,以逻辑正常分布的活动和突触权重.
主要方法:
- 分析公式的推导,以提供相互信息.
- 重尾 (lognormal) 和短尾分布之间的比较.
主要成果:
- 来自网络元件之间相互信息的分析公式.
- 与短尾神经网络相比,在lognormal神经网络中,相互信息通常更大.
- 在逻辑正常神经网络中,相互信息可以在有限的差异上分离.
结论:
- 在神经活动和突触权重中的重尾逻辑正常分布增强了相互信息.
- 进化可能有利于异质的神经动力学,以优化信息处理.
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