Self-organization in a parametrically coupled logistic map network: a model for information processing in the visual cortex.
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.
Pashaie
Ramin
R
Department, Stanford University, Stanford, CA 94305 USA. raminp@stanford.edu
Farhat
Nabil H
NH
D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D007257:Information Theory; D016015:Logistic Models; D008322:Mammals; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D010775:Photic Stimulation; D009435:Synaptic Transmission; D014793:Visual Cortex / Q000033:anatomy & histology / Q000502:physiology*; D014795:Visual Pathways / Q000033:anatomy & histology / Q000502:physiology; D014796:Visual Perception / Q000502:physiology*