Karbowski Jan
Institute of Applied Mathematics and Mechanics, Department of Mathematics, Informatics and Mechanics, University of Warsaw, 02-097 Warsaw, Poland.
Entropy (Basel). 2024 Sep 11;26(9):779. doi: 10.3390/e26090779.
This paper provides a perspective on applying the concepts of information thermodynamics, developed recently in non-equilibrium statistical physics, to problems in theoretical neuroscience. Historically, information and energy in neuroscience have been treated separately, in contrast to physics approaches, where the relationship of entropy production with heat is a central idea. It is argued here that also in neural systems, information and energy can be considered within the same theoretical framework. Starting from basic ideas of thermodynamics and information theory on a classic Brownian particle, it is shown how noisy neural networks can infer its probabilistic motion. The decoding of the particle motion by neurons is performed with some accuracy, and it has some energy cost, and both can be determined using information thermodynamics. In a similar fashion, we also discuss how neural networks in the brain can learn the particle velocity and maintain that information in the weights of plastic synapses from a physical point of view. Generally, it is shown how the framework of stochastic and information thermodynamics can be used practically to study neural inference, learning, and information storing.
本文提供了一种观点，即把非平衡统计物理中最近发展起来的信息热力学概念应用于理论神经科学问题。从历史上看，神经科学中的信息和能量是分开处理的，这与物理学方法形成对比，在物理学方法中，熵产生与热的关系是一个核心概念。本文认为，在神经系统中，信息和能量也可以在同一个理论框架内进行考虑。从经典布朗粒子的热力学和信息论的基本思想出发，展示了有噪声的神经网络如何推断其概率运动。神经元对粒子运动的解码具有一定的准确性，并且有一定的能量消耗，两者都可以用信息热力学来确定。以类似的方式，我们还从物理角度讨论了大脑中的神经网络如何学习粒子速度并将该信息保存在可塑性突触的权重中。一般来说，展示了随机和信息热力学框架如何实际用于研究神经推理、学习和信息存储。
