BRAIN 

Understanding how individual neurons contribute to network functions is fundamental to neuroscience. Recent years have seen exciting progresses in the reconstructions of single-neuron morphologies and wiring diagrams at the level of individual synapses. Although these progresses offer promises of understanding neuronal networks, such understandings would not be reached if we do not tackle how the structure of single neurons contribute to the network connectivity. Despite neuronal network connectivity has been studied in depth using graph theory and other mathematical approaches, most computational models have disregarded morphological features involved in network connectivity. For the few that did, the methods developed are either unavailable to the broad community or not user-friendly, preventing further investigations on experimental structural data and network modelling. This project aims to develop and validate a user-friendly toolset that link neuronal morphology to network connectivity, allowing us to predict neural network properties from single-neuron morphologies. 

Such open-source computational tool will involve methods for data visualization and analysis of neuronal populations derived from whole brain imaging data. Also, it will provide an innovative generative model for interrogation of the organizational principles underlying brain networks’ architecture and to explore potentially relevant network properties.