This project focuses on developing a new computational framework to uncover the fundamental principles governing how brain networks grow and self-organise.
This is a computational project grounded in the direct analysis of complex biological data. While we can record neuronal activity, these recordings are like isolated snapshots. This project will address this challenge by pioneering a dynamic Bayesian inference model, grounded in statistical physics, to analyse state-of-the-art biological datasets, specifically high-resolution multielectrode array (MEA) recordings from developing human cortical networks.
The key innovation is to create a "movie" from these snapshots, inferring the evolving network structure by tracking how synaptic connections grow, strengthen, and prune over days. The researcher will work at the interface of advanced theory and challenging biological data analysis, interacting closely with experimental neurobiologists. The ultimate goal is to extract the underlying rules of human neuronal plasticity, providing foundational knowledge to inform the development of novel neuromorphic computing architectures.