Improving Dendritic Neuron Model With Dynamic Scale-Free Network-Based Differential Evolution

Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are well-tuned by a learning algorithm.However,the back-propagation algorithm(BP),as a mostly used learning algorithm,intrinsically suffers from defects of slow convergence and easily dropping into local minima.Therefore,more and more research adopts non-BP learning algorithms to train ANNs.In this paper,a dynamic scale-free network-based differential evolution(DSNDE)is developed by considering the demands of convergent speed and the ability to jump out of local minima.The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem.Nine meta-heuristic algorithms are applied into comparison,including the champion of the 2017 IEEE Congress on Evolutionary Computation(CEC2017)benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase(EBOwithCMAR).The experimental results reveal that DSNDE achieves better performance than its peers.

Specific effects of c-Jun NH2-terminal kinaseinteracting protein 1 in neuronal axons

c-Jun NH2-terminal kinase（JNK）-interacting protein 3 plays an important role in brain-derived neurotrophic factor/tropomyosin-related kinase B（Trk B） anterograde axonal transport. It remains unclear whether JNK-interacting protein 1 mediates similar effects, or whether JNK-interacting protein 1 affects the regulation of Trk B anterograde axonal transport. In this study, we isolated rat embryonic hippocampus and cultured hippocampal neurons in vitro. Coimmunoprecipitation results demonstrated that JNK-interacting protein 1 formed Trk B complexes in vitro and in vivo. Immunocytochemistry results showed that when JNK-interacting protein 1 was highly expressed, the distribution of Trk B gradually increased in axon terminals. However, the distribution of Trk B reduced in axon terminals after knocking out JNK-interacting protein 1. In addition, there were differences in distribution of Trk B after JNK-interacting protein 1 was knocked out compared with not. However, knockout of JNK-interacting protein 1 did not affect the distribution of Trk B in dendrites. These findings confirm that JNK-interacting protein 1 can interact with Trk B in neuronal cells, and can regulate the transport of Trk B in axons, but not in dendrites.