论文标题
自由能原理驱动神经形态发展
The Free Energy Principle drives neuromorphic development
论文作者
论文摘要
我们展示了任何具有自由度和局部自由能的系统如何在自由能原理的限制下，都将发展为神经形态的形态，该神经形态形态支持层次结构计算，在该计算中，层次结构的每个级别都会构成其输入的粗糙度，并在其输出中表现出良好的输出。从细胞内信号转导途径的体系结构到哺乳动物大脑中的感知和动作周期的大规模组织，这种层次结构发生在整个生物学中。正式地，一方面是锥体 - 康基图（CCCD）作为量子参考框架模型，另一方面是CCCD和拓扑量子场理论之间的密切形式连接，允许在完全量化的量子量子量子量子神经网络中代表此类计算。
We show how any system with morphological degrees of freedom and locally limited free energy will, under the constraints of the free energy principle, evolve toward a neuromorphic morphology that supports hierarchical computations in which each level of the hierarchy enacts a coarse-graining of its inputs, and dually a fine-graining of its outputs. Such hierarchies occur throughout biology, from the architectures of intracellular signal transduction pathways to the large-scale organization of perception and action cycles in the mammalian brain. Formally, the close formal connections between cone-cocone diagrams (CCCD) as models of quantum reference frames on the one hand, and between CCCDs and topological quantum field theories on the other, allow the representation of such computations in the fully-general quantum-computational framework of topological quantum neural networks.
