Plasticity Neural Network Based on Astrocytic Influence at Critical Period, Synaptic Competition and Compensation by Current and Mnemonic Brain Plasticity and Synapse Formation

The mechanism of our NN is in line with the results of the latest MIT brain plasticity study, in which researchers found that as a synapse strengthens, neighboring synapses automatically weaken themselves to compensate. Regarding this mechanism, Dr. Luo's team at Stanford University has put forward that competition regarding synapse formation for dendritic morphogenesis is crucial. The astrocyte impacts on Brain Plasticity and Synapse Formation is an important mechanism of our NN at critical period and closure of critical period. We try to conduct research of failure in brain plasticity by model at the closure of critical period by contrasting with studies before. Cutting edge imaging and genetic tools are combined in their studies, whereas our research lays more emphasis on a new NN. In tests, possible explanations of dendrite morphogenesis are derived, which demonstrate that dendrite generation, to a certain extent, is curbed by synapse formation. Current and mnemonic brain plasticity as well as synaptic action range are also taken into account in the study. Furthermore, the frame of NN is based on current and mnemonic dynamic gradient informational synapse formation. The mnemonic gradient information needs to take into account the forgotten memory-astrocytic synapse formation memory factor. Mnemonic brain plasticity involves the plus or minus disturbance-astrocytic brain plasticity phagocytose factor. The influence of astrocyte made local synaptic action range remain in an appropriate length at critical period. Through the tabular data of the PNN test, we found that the memory factor of astrocytes, like the phagocytose factor, produces the effect of reducing the local accumulation of synapses. Therefore, is it possible to reduce the number of animal experiments and their suffering by simulating and planning the factors of biological experiments through Deep Learning models?