【学术报告】Predicting wiring length distribution of brain networks using the maximum entropy principle

发布者:孙毅 发布时间:2021-04-26 浏览次数:303

报告人:

上海交通大学 李松挺 副教授


报告摘要:

A brain network in general comprises a substantial amount of shortrange connections with an admixture of long-range connections. Despite this common feature, the portion of long-range connections between brain areas or neurons are observed to be quantitatively dissimilar for different species. It is hypothesized that the length of connections is constrained by the spatial embedding of brain networks, yet fundamental principles that underlie the wiring length distribution remain to be elucidated. In this talk, by quantifying the structural diversity of a brain network using the measure of Shannon’s entropy, we will show that the wiring length distribution across multiple species follows the maximum entropy principle under the constraints of limited wiring material and the spatial locations of brain areas or neurons. In addition, we will develop a generative model incorporating the maximum entropy principle, and will show that, for the five species, the network reconstructed by the generative model exhibits higher similarity to the real network compared with those reconstructed by alternative models without accounting for the aspect of entropy. Our work indicates that the connectivity of brain networks evolves to be structurally diversified by maximizing entropy in order to support its complicated functions such as efficient inter-areal communication, thus provides a potential organizational principle of spatially embedded brain networks.