In addition to the shared weights of the synaptic connections, we proposed a new neural network that includes the synaptic effective range weights for both the forward and back propagation. And lots of simulations were used which RNN cannot be achieved. The simulations of PNN fit very well in experiments and hypotheses of 6 papers CNS Journals, 6 papers of CNS family Journals and 1 paper top Physics Journal [14-26]. The brain plasticity in positive or negative memory may be quantum and produce short-term memory, and exhibits an exponential decay in the wave function over a period of time, produced in the hippocampus. And exponential decay occurs due to barriers, and barriers can refer to astrocytes. Brain plasticity in working memory flows through the brain, from the hippocampus to the cortex, through directional derivatives. The strong working memory brain plasticity turns to long-term memory means maximum of directional derivatives, and maximum of directional derivatives is gradient. Thus, long-term memory signifies the gradient of brain plasticity in working memory. 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. This could be the process of astrocytes phagocytose synapses is driven by both positive and negative memories of plasticity in the brain. In simulation, it is possible that thicker cortices and more diverse individuals within the brain could have high IQ, but thickest cortices and most diverse individuals may have low IQ in simulation. PSO considers global solution or best previous solution, but also considers relatively good and relatively inferior solution. And PNN modified ResNet to consider memory gradient. The simple PNN only considers astrocytes phagocytosed synapses.