Editorial: Machine learning for computational neural modeling and data analyses

Youhui Zhang, Yiran Chen, Yuanyuan Mi

Abstract: Specifically, for neuroscience and computational neuroscience researchers, the requirements contain generating biological neural parameters and synaptic connectivity from experimental data, as well as processing other biophysical models and data. On the other hand, AI can make fundamental contributions to computational neural modeling and data analyses, which can help us gain insight into understanding the inner mechanisms of biological neural network and so on. Anyway, we hope to inspire machine learning researchers to generate efficient methods and tools for processing neuroscience data, so as to change the latter research paradigm.Therefore, the purpose of this Research Topic is bridging the gap between neuroscience researchers investigating biological models and computer scientists developing brain-inspired methodologies and tools, covering the entire process of modeling and analysis from data cleaning and preparation to modeling, and to simulation and optimization.Six papers in this special issue have been accepted, which involve applying machine learning methods to connectomics, EEG information processing, synaptic tagging and capture (STC), inverse muscle models, neuronal behavior prediction, and more, respectively. The below is an overview and discussion of the accepted articles.Wang et al. proposed a simple artificial neural network (ANN) to predict spike features of Hodgkin-Huxley-type neurons. Compared with previous work, it can evaluate the informative features...