改进最小自由能量原则,以最大信息效率原则
Chenguang Lu1,2
1Intelligence Engineering and Mathematics Institute, Liaoning Technical University, Fuxin 123000, China.
Entropy (Basel, Switzerland)
|July 29, 2025
概括
这项研究引入了语义变量贝叶斯学 (SVB) 和最大信息效率 (MIE) 原则,为理解大脑和行为与环境的协调提供了一个比原来的最小自由能量原则 (FEP) 更强大的框架.
科学领域:
- 神经科学是一个神经科学.
- 信息理论 信息理论
- 计算生物学 计算生物学
背景情况:
- 弗里斯顿的最小自由能量原理 (FEP) 基于变量贝叶斯式 (VB) 方法,表明大脑和行为与环境自我组织.
- FEP具有理论上的局限性,包括潜在的误解和局限性,仅限于概率函数.
研究的目的:
- 通过引入语义变量贝叶斯式 (SVB) 和最大信息效率 (MIE) 原则来解决FEP的局限性.
- 为了解大脑与环境的相互作用和积极推断提供一个更全面的框架.
主要方法:
- 介绍语义信息G理论和R(G) 函数,使用P-T概率框架.
- 逻辑贝叶斯推理的应用和R (G) 函数的分析.
- 理论分析和计算实验以验证拟议的SVB和MIE原则.
主要成果:
- 证明R - G = F - H{\displaystyle X{\displaystyle Y} ,其中F是变量自由能量 (VFE) 和H{\displaystyle X}Y是香农条件,挑战了不断下降F的概念.
- SVB被证明是一种可靠和简单的方法,用于隐性变量分析和主动推理.
- 澄清不同热力学系统中Shannon信息,语义信息,VFE,自由能量,exergy和条件之间的关系.
结论:
- 与FEP相比,SVB和MIE提供了进步,为生物系统中信息处理提供了更细致的理解.
- 拟议的原则提高了自由能源原则的解释性和适用性.
- 建议将深度学习方法与深度学习方法集成,以更广泛地应用MIE原则.
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