The observation of interactions between neurons of a network can reveal important information about how information is processed within that network. Such observation can be established with the analysis of causality between the activities of the different neurons in the network. This analysis is called effective connectivity analysis. However, methods for such analysis are either computationally heavy for daily use or too inaccurate for making reliable analyses. Cox method produces reliable analysis, but the computation takes hours on CPUs, making it slow to use on research. In this paper, two algorithms are presented that speed up analysis of Cox method by parallelizing the computation on a graphical processing unit (GPU) with the help of a Compute Unified Device Architecture platform. Both algorithms are evaluated according to the network size and recording duration. The interest of proposing GPU implementations is in gaining the computation time but another important interest is that such implementation requires rethinking the algorithm in different ways than as the sequential implementation. This rethinking itself brings new optimization possibilities, e.g. by employing OpenCL. Utilizing this accelerated implementation, the Cox method is then applied on an experimental dataset from CRCNS in a personal computer. This should facilitate observations of biological neural network organizations that can provide new insights to improve understanding of memory, learning and intelligence.
['Vafa Andalibi', 'François Christophe', 'Teemu Laukkarinen', 'Tommi Mikkonen']
Effective Connectivity Analysis in Brain Networks: A GPU-Accelerated Implementation of the Cox Method