Complex dynamics for the study of neural activity in the human brain

Neural mass modeling is a part of computational neuroscience that was developed to study the general behavior of interacting neuronal populations. This type of mesoscopic model is able to generate output signals that are comparable with experimental data such as electroencephalograms. Classically, neural mass models consider two interconnected populations. One interaction have been modeled in two differents ways. In this work we propose and analyze a neural mass model embedding both approaches and compare the generated time series to real data.