Work was conducted via 6 work packages (WPs), of which, 5 were completed (WP1, WP2, WP4, WP5 and WP6). WP1 sought to build feed-forward models that could approximate the EEG signal from networks of simplified neuron models. Our feed-forward models were shown to predict well the EEG signal across different configurations of the cortical network. It yielded a journal publication in PLOS Computational Biology. Results of WP1 were also presented in the Neuromatch Conference 3.0. WP2 involved developing algorithms for inferring changes in parameters of the neural circuit caused by changes in the EEG signal. The Fellow delivered a journal article in Brain Informatics that shows results for WP2. There is another forthcoming journal article that includes more detailed findings of WP2. In WP4, we successfully validated our inference methods on empirical data from mice. In particular, the mathematical methods developed in WP2 were validated with data that combined electrophysiological recordings in mice with chemogenetic perturbations to manipulate neural activity. The Fellow exceeded goals by validating the inference models on magnetoencephalogram (MEG) data of human subjects (data provided by another project). Results of WP4 will be included in the forthcoming journal article. In WP5, the Fellow published in high quality, open-access international journals and international conferences, uploaded the source code to public open-source repositories, as well as delivered different public engagement activities to popularize and communicate findings of the action (such as the European Researchers’ Night 2021 or the IIT international talks). In WP6, for researcher training, the Fellow attended different training workshops, conferences, lab meetings, journal clubs and seminars of different international labs in different countries. In WP6, the Fellow participated in the research and financial management of the action and developed the Career Development Plan (CPD).