Tracing the Flow of Perceptual Features in an Algorithmic Brain Network

• DOI: 10.1038/srep17681
• Publication Date: 2015-12-04T10:19:59Z
• Authors: Robin A. A. Ince; Nicola J. van Rij...; Gregor Thut; Guillaume A. Rous...; Joachim Gross; Stefano Panzeri; Philippe G. Schyns
• Keywords: Information flow; Theory of computation; Computational neuroscience; Feature (linguistics); Computational model; Information Theory; Tracing

Abstract The model of the brain as an information processing machine is a profound hypothesis in which neuroscience, psychology and theory computation are now deeply rooted. Modern neuroscience aims to network densely interconnected functional nodes. However, dynamic mechanisms perception cognition, it imperative understand networks at algorithmic level–i.e. flow that nodes code communicate. Here, using innovative methods (Directed Feature Information), we reconstructed examples possible communicate specific features underlying two distinct perceptions same ambiguous picture. In each observer, identified architecture comprising one occipito-temporal hub where both perceptual decisions dynamically converge. Our focus on detailed represents important step towards new algorithmics cognition.