During the past decades, significant advances have been accomplished in the development of neuroimaging techniques allowing for more and more accurate recording of neuronal systems (in terms of temporal and spatial resolution). For example, surface electrophysiology (EEG, MEG) or functional MRI have led to a vast literature on human brain mapping. Meanwhile, progress has also been made in the understanding of the basic mechanisms involved in excitation-, inhibition- and synchronization-related processes in brain neuronal systems, at sub-cellular (membrane ion channels, neurotransmitter receptors), cellular (neurons) and network (assemblies of neurons) levels. However, the characterization of such mechanisms from non-invasive methods is still considered as a difficult and unsolved problem. Difficulties arise from: i) the large diversity of neuroimaging techniques that are now available to record from neuronal systems ii) the fact that each technique can only provide, by itself, a partial, specific and indirect measurement of the activity in considered systems, which needs to be integrated in order to provide one global picture of brain activity iii) the incomplete knowledge about the neurophysiological and the biophysical aspects involved in the generation of observations (local or global electric field potentials, magnetic fields, Blood-oxygen-level dependent - BOLD - responses, oxygen - O2 - rates, …). In this context, computational models provide a unified framework in which knowledge on the physiology of neuronal activity and neurovascular coupling can be incorporated, in order to simulate the output signals for given parameters. The parameters range can be inspired from physiology-based studies in actual animal models. Once the parameter range is defined, one can test the influence of varying a reduced set of parameters (for example, ratio between excitation and inhibition) on the output signals. In some conditions, the models can be inverted and hidden variables, i.e. variables not directly measured but contained in the models such as ratio excitation/inhibition, synchronization across populations of neurons, can be recovered from raw data using parameter identification procedures. In the current project, we propose to develop computational models in order to simulate neuroimaging data, and to compare the results of these models to multimodal datasets obtained in animals and humans. This will permit to improve the interpretation of such data and increase the quantity of information that can be extracted. We will operate at the level of population of cell, i.e. at a scale compatible with the resolution of neuroimaging tools (at the level of the mm). We propose a novel model structure, which will include astrocytes at this “mesoscopic” level and will operate in networks of connected regions. Moreover, we will compare models in physiological and pathological conditions, which will be a step towards a better understanding of mechanisms underlying epileptic condition. The MULTIMODEL project stems from a conjoint Inserm-Inria scientific initiative launched in December 2008. It is entitled « Models and interpretation of multimodal data (EEG, MEG, fMRI). Application to epilepsy » and involves 5 partners (Inserm U751 in Marseille, U678 in Paris, U836 in Grenoble, U642 in Rennes and INRIA Odyssée project-team in Sophia Antipolis). The current proposal would permit to continue the promising work started by this initiative.