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

Beyond the consideration of the basic connection, this research focus on a new Neutral Network (NN) model, referred as PNN (Plasticity Neural Network) in which the current and mnemonic range of action between synapses and neurons plastic changes with iteration are both taken into account at the same time, speciﬁ-cally, the current and mnemonic synaptic range of action weights. In addition, the coding result on PNN mode is given that as one synapse strengthens, neighboring synapses weaken on their own to compensate it; this mechanism is consistent with the ﬁndings in the recent research on brain plasticity at the MIT Picower Institute. Regarding the importance of this mechanism, Dr. Luo’s team at Stanford University has mentioned that the competition regarding synapse formation for dendritic morphogenesis is important. The inﬂuence of astrocyte impacts on brain plasticity and synapse formation is an important mechanism of our Neural Network at critical periods and the end of critical periods. We try to examine the mechanism of failure in brain plasticity by model at the end of critical periods in details by contrasting study before [17]. The new PNN model is not just modiﬁed on the frame of NN based on current gradient informational synapse formation and brain plasticity, but also the mnemonic gradient informational synapse formation and brain plasticity at critical periods. The mnemonic gradient information needs to consider forgotten memory-astrocytic synapse formation mnemonic factor. And mnemonic brain plasticity involves plus or minus disturbance-astrocytic brain plasticity disturbance factor. synaptic position is ﬁne-tuned in every iteration. The range of synaptic changes cannot be too large, which must retain within a minimum value for each synaptic formation, thus making the synaptic range of action more sensitive to the plasticity than the elasticity. The cosine ﬁlter is used in the tests for comparing the result of the three cases, namely gradient optimized synaptic range (ORPNN), random synaptic range (RRPNN), and constant synaptic range (CRPNN). The result indicates that the OPRNN runs the best. The iteration of CRPNN method indicates diﬃcult to converge. RRPNN method is slower convergent than ORPNN method on ﬁnding the optimal solution. Considering the insights from the practical pandemic, we attempted to explain the PNN model. A simulation of PNN was conducted on the cognitive processes in the brain from infancy to senile phase, which is interpreted by the observation of decreasing the synapse population and increasing the minimum of synaptic range of action in PNN evolution of synapse formation, hence the loss of diversity and plasticity of cognition. This explanation is similar to the Dr. Luo and his colleagues’ research, and on PNN which shows that the synapse formation in a certain extent may inhibit dendrite. And we also analyzed the diﬀerent brain plasticity by tables at critical and the end of critical periods. Furthermore, the RNN-based PNN formula is simple and easy to implement; and the contribution to NN is as seminal as BP. Most of our simulations did not refer the biological tests and papers besides hypothesis of Dr. Luo’s team in synapse formation, and our question is if model construction, formula derivation and algorithm testing to promote the development of neuroscience and brain cognition? And it has no ethics of animal testing. the PNN test, we found that the memory persistence 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? Furthermore, the RNN-based PNN formula is simple and easy to implement; and the contribution to NN is as seminal as BP. Most of our simulations did not refer to the biological tests and papers besides the hypothesis of Dr. Luo’s team in synapse formation, and our question is can model construction, formula derivation and algorithm testing serve to promote the development of neuroscience and brain cognition?