Self-Organization in a Parametrically Coupled Logistic Map Network: A Model for Information Processing in the Visual Cortex

• Published in: IEEE transaction on neural networks and learning systems 2009-04, Vol.20 (4), p.597-608
• Main Authors: Pashaie, R., Farhat, N.H.
• Format: Article
• Language: English
• Subjects: Action Potentials; Algorithms; Anatomy; Animals; Applied sciences; Artificial intelligence; Brain modeling; Cerebral cortex; Chaos; Cognition; Computer science; control theory; systems; Computer Simulation; cortex; cortical maps; Exact sciences and technology; Information processing; Information Theory; Logistic Models; Logistics; Mammals; Models, Neurological; Neurons - physiology; Nonlinear Dynamics; Orbits; Pattern recognition. Digital image processing. Computational geometry; Photic Stimulation; self-organization; Synaptic Transmission; Unsupervised learning; Visual Cortex - anatomy & histology; Visual Cortex - physiology; Visual Pathways - anatomy & histology; Visual Pathways - physiology; Visual Perception - physiology
• ISSN: 1045-9227; 2162-237X; 1941-0093; 2162-2388
• DOI: 10.1109/TNN.2008.2010703

In this paper, a new model seeking to emulate the way the visual cortex processes information and interacts with subcortical areas to produce higher level brain functions is described. We developed a macroscopic approach that incorporates salient attributes of the cortex based on combining tools of nonlinear dynamics, information theory, and the known organizational and anatomical features of cortex. Justifications for this approach and demonstration of its effectiveness are presented. We also demonstrate certain capabilities of this model in producing efficient sparse representations and providing the cortical computational maps.