  We firstly simulated disease dynamics by KAN (Kolmogorov-Arnold Networks)
nearly 4 years ago, but the kernel functions in the edge include the
exponential number of infected and discharged people and is also in line with
the Kolmogorov-Arnold representation theorem, and the shared weights in the
edge are the infection rate and cure rate, and used activation function by tanh
at the node of edge. And this Arxiv preprint version 1 of March 2022 is an
upgraded version of KAN, considering the invariant coarse-grained which
calculated by residual or gradient of MSE loss. The improved KAN is PNN
(Plasticity Neural Networks) or ELKAN (Edge Learning KNN), in addition to edge
learning, it also considered the trimming of the edge. We not inspired by the
Kolmogorov-Arnold representation theorem but inspired by the brain science. The
ELKAN to explain brain, the variables correspond to different types of neurons,
the learning edge can be explained by rebalance of synaptic strength and glial
cells phagocytose synapses, and the kernel function means the discharge of
neurons and synapses, different neurons and edges mean brain regions. Through
testing by cosine, the ELKAN or ORPNN (Optimized Range PNN) is better than the
KAN or CRPNN (Constant Range PNN).The ELKAN is more general to explore brain,
such as mechanism of consciousness, interactions of natural frequencies in
brain regions, synaptic and neuronal discharge frequencies, and data signal
frequencies; mechanism of Alzheimer's disease, the Alzheimer's patients has
more high frequencies in the upstream brain regions; long short-term relatively
good and inferior memory which means gradient of architecture and architecture;
turbulent energy flow in different brain regions, turbulence critical
conditions need to be met; heart-brain of the quantum entanglement may occur
between the emotions of heartbeat and the synaptic strength of brain
potentials.
