In addition to weights of shared synaptic connections, we proposed a new Neural Network that includes weights of synaptic effective ranges for Forward propagation and Back propagation. And using more simulations which RNN cannot be achieved. The simulations of PNN fit very well in brain science experiments and hypotheses of 6 papers CNS, 6 papers of CNS family Journals and 1 paper top Physics Journal [14-26]. Positive or negative working or short-term memory brain plasticity is quantum, and exhibits exponential decay of wave function for a while, produced in the hippocampus. And exponential decay because of barriers, and barriers may relate to astrocytes. Working memory brain plasticity go through brain from hippocampus to cortices by directional derivative. The strong working memory brain plasticity turns to long-term memory means maximum of directional derivatives, and maximum of directional derivatives is gradient. So long-term memory means gradient of working memory brain plasticity. The process of short-term memory turns to long-term memory is the process of non-classically turns to classically. Astrocytic cortex memory persistence factor also inhibits local synaptic accumulation, and the model inspires experiments. It may be the process of astrocytes phagocytose synapses is driven by positive and negative memories of brain plasticity. The thicker cortex and the more diverse individuals in brain may have high IQ in simulation, but the thickest cortex and the most diverse individuals may have low IQ in simulation. PSO considers global solution or best previous solution, but PNN also considers relatively good and relatively inferior solution. And PNN modified ResNet to calculate the layers' number, is proposed the gradient of the weight is considered not only the current but also the memory gradient by formula derivation. The simple PNN only has the astrocytes phagocytose synapses.