Estimation of small-world neuronal network topology based on relevance vector machine
基于相关向量机的小世界神经元网络拓扑估计

This paper applied relevance vector machine to estimate node dynamical equations and topology of small-world neuronal network from noisy time series. According to the fact that many dynamical equations or its power series expansion had polynomial structure, by constructing a unified polynomial and making the original dynamical equation sparse in the unified polynomial, obtained dynamical equations and network topology obtained while used sparse Bayesian learning to estimate the sparse nonzero terms. FHN small-world neuronal network as a paradigm demonstrated the estimation effect of the dynamical equations and network topology. The results show that the estimating strategy can identify equations structure and network topology accurately and quickly, the error is small in dynamical equations coefficients and couple strength estimation and is robust to noise.