One of my main current interest is in the model-based analysis of fMRI data in order to explore the brain coordination when human perform a task: selection of specific pathways for sensory integration, global change such as arousal. The aim of the framework is to uncover causal interactions (effective connectivity, EC) between brain regions that are modified for given conditions (e.g., rhythm listening, movie viewing), moving beyond a simple phenomenological description of their observed correlations. I also plan to extend the existing framework to interpret EEG and MEG data.Gilson et al. (2016) PLoS Comput Biol 12: e1004762 (model optimization)
My MSCA project applies the network-specific analysis to multiunit activity recorded in monkeys in collaboration with Prof Alex Thiele (Newcastle University). The goal is to compare the effective connectivity estimated from observed network activity to a-priori knowledge about anatomical connections.
With Adrià Tauste Campo from Gustavo's CNS lab at UPF, we've developed a non-parametric method for connectivity estimation from observed network activity, based on the multivariate autoregressive (MVAR) process, which can be seen as an alternative to Granger causality analysis.Gilson et al. (in press) Network Neurosci; preprint on biorxiv
I am still interested in the dynamics of spiking networks, with and without plasticity (e.g., STDP). Mathematical models formalise how the network structure and neural dynamics determine plasticity, which in turns modifies the network dynamics. I aim to extend this framework to function and coding.Gilson et al. (2010) Front Comput Neurosci 4: 23