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 very well 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 the importance of this mechanism, Dr. Luo's team at Stanford University has put forward that competition regarding synapse formation for dendritic morphogenesis is crucial. We try to conduct research on the mechanism of failure in brain plasticity by model at the closure of critical period in details by contrasting with studies before. Cutting edge imaging and genetic tools are combined in their experimental studies, whereas our research lays more emphasis on the model, derivation and simulation of a new NN. In tests, 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 the new NN is based on current gradient informational and mnemonic negative and positive gradient informational synapse formation. The mnemonic gradient information needs to take into account the forgotten memory-astrocytic synapse formation memory persistence factor (including both negative and positive memories - i.e. the optimal gradient information so far and relatively inferior gradient information). We found that the astrocytic memory persistence factor, like the phagocytosis factor, produces the effect of reducing the local accumulation of synapses. The PNN in which only the synaptic phagocytosis effect is considered regardless of the gradients update, and whether the synaptic phagocytosis of different variables and synaptic positions is cancelled is determined by the correlation coefficient of the corresponding time interval, proves simple and effective.