CellularFlow: Information will be updated. https://ieeexplore.ieee.org/document/9924170 Paper abstract: Now is the age of neuromorphic computing that creates brain circuits. The analog and digital circuit theory changes because the values of the basic conductance elements can be made variable by learning. The computer structure changes to in-memory computing technology in cooperation with a von Neumann architecture. In this paper, we propose a gyrator neuron (GN) that enables analog computer operations by nodal equation. The GN is constructed based on memristor elements. The GN executes learning by back propagation processing and association by forward propagation processing. Postscript: Basic Operation is known as "Transformer" in a large discrete AI LLM system. The Gyrator Neuron is the smallest "Transformer" with a teacher signal (Query) and hidden node signals ( Key, Value) corresponding to the SoftMax function in which Q and K approach each other. Memristance is determined at its equilibrium point of the phase change.