通过最大信息处理的统一原则,两个对大脑的看法得到了协调
Leandro J Fosque1,2, Woodrow L Shew3, ShiNung Ching4
1Department of Biology, Washington University in Saint Louis, St. Louis, MO, USA.
bioRxiv : the preprint server for biology
|December 15, 2025
概括
大脑可以通过批判性来实现最佳的计算和适应性,这一原则现在已经定义为低维神经模型. 这一发现将各种计算神经科学方法统一在一个大脑功能的通用框架下.
科学领域:
- 计算神经科学是一种神经科学.
- 理论神经科学 理论神经科学
- 系统神经科学 系统神经科学
背景情况:
- 生物系统,特别是大脑,面临着对提高计算能力和适应能力的选择性压力.
- 批判性已经成为生物系统中计算的统一框架,但以前仅限于高维模型.
- 这种限制在高维的关键性框架和神经科学基础的低维模型之间造成了差距.
研究的目的:
- 为动态系统开发一个超越维度的数学定义的关键性.
- 调查神经模型中关键性的来源和普遍性.
- 确定关键性是否是一个统一的原则,在不同的模型维度中管理大脑功能.
主要方法:
- 开发了一种基于时间尺度不变性的关键性的新数学定义.
- 在神经模型中分析了分叉,以确定关键性的来源 (确定性与噪音驱动).
- 进行了数值分析,以评估关键分叉处的信息处理能力,并评估了历史的神经模型.
主要成果:
- 引入了适用于任何维度的动态系统的关键性的定义.
- 确定了两种不同的机械学来源在分叉处的关键性.
- 证明信息处理能力在关键分叉处得到最大化,一些正规的神经模型表现出生物学上可信的关键性.
结论:
- 批判性是一种通用的神经生物学原理,可以跨越所有模型维度.
- 这项工作将计算神经科学中的不同建模方法统一在一个框架下.
- 最佳的信息处理源于关键动态的基本特性,而不是特定模型的机制.
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