Independent component analysis for functional neuronal interactions

Blind separation of independent sources or independent component analysis (ICA) has received a great deal of attention in the field of neurobiological data analysis such as EEG, MEG, fMRI. In this paper, we present a novel result for identification of neuronal ensemble interactions by applying ICA approach. The experimental results are obtained based on the cortical circuit model. Several kinds of neuronal activities such as the back-ground and afferent activities have been identified successfully. This result suggests that the source signals are represented in the correlated firing patterns within the specific range. Neuronal activities can be detected when high-order correlations between them are quantified by ICA.