Hylton Todd
Department of Electrical and Computer Engineering, University of California, San Diego, CA 92093, USA.
Entropy (Basel). 2020 Feb 25;22(3):256. doi: 10.3390/e22030256.
A thermodynamically motivated neural network model is described that self-organizes to transport charge associated with internal and external potentials while in contact with a thermal reservoir. The model integrates techniques for rapid, large-scale, reversible, conservative equilibration of node states and slow, small-scale, irreversible, dissipative adaptation of the edge states as a means to create multiscale order. All interactions in the network are local and the network structures can be generic and recurrent. Isolated networks show multiscale dynamics, and externally driven networks evolve to efficiently connect external positive and negative potentials. The model integrates concepts of conservation, potentiation, fluctuation, dissipation, adaptation, equilibration and causation to illustrate the thermodynamic evolution of organization in open systems. A key conclusion of the work is that the transport and dissipation of conserved physical quantities drives the self-organization of open thermodynamic systems.
描述了一种具有热力学动机的神经网络模型，该模型在与热库接触时会自我组织以传输与内部和外部电势相关的电荷。该模型整合了用于节点状态快速、大规模、可逆、保守平衡以及边缘状态缓慢、小规模、不可逆、耗散适应的技术，以此作为创建多尺度秩序的一种手段。网络中的所有相互作用都是局部的，并且网络结构可以是通用的和循环的。孤立的网络表现出多尺度动力学，而外部驱动的网络会进化以有效地连接外部正电势和负电势。该模型整合了守恒、增强、波动、耗散、适应、平衡和因果关系等概念，以说明开放系统中组织的热力学演化。这项工作的一个关键结论是，守恒物理量的传输和耗散驱动了开放热力学系统的自组织。
