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We firstly simulated disease dynamics by KAN (Kolmogorov-Arnold Networks)nearly 4 years ago, but the kernel functions in the edge include theexponential number of infected and discharged people and is also in line withthe Kolmogorov-Arnold representation theorem, and the shared weights in theedge are the infection rate and cure rate, and used activation function by tanhat the node of edge. And this Arxiv preprint version 1 of March 2022 is anupgraded version of KAN, considering the invariant coarse-grained whichcalculated by residual or gradient of MSE loss. The improved KAN is PNN(Plasticity Neural Networks) or ELKAN (Edge Learning KNN), in addition to edgelearning, it also considered the trimming of the edge. We not inspired by theKolmogorov-Arnold representation theorem but inspired by the brain science. TheELKAN to explain brain, the variables correspond to different types of neurons,the learning edge can be explained by rebalance of synaptic strength and glialcells phagocytose synapses, and the kernel function means the discharge ofneurons and synapses, different neurons and edges mean brain regions. Throughtesting by cosine, the ELKAN or ORPNN (Optimized Range PNN) is better than theKAN or CRPNN (Constant Range PNN).The ELKAN is more general to explore brain,such as mechanism of consciousness, interactions of natural frequencies inbrain regions, synaptic and neuronal discharge frequencies, and data signalfrequencies; mechanism of Alzheimer's disease, the Alzheimer's patients hasmore high frequencies in the upstream brain regions; long short-term relativelygood and inferior memory which means gradient of architecture and architecture;turbulent energy flow in different brain regions, turbulence criticalconditions need to be met; heart-brain of the quantum entanglement may occurbetween the emotions of heartbeat and the synaptic strength of brainpotentials.

The Deep Learning model of Higher-Lower-Order Cognition, Memory, and Affection- More General Than KAN