title: Hierarchical random cellular neural networks for system-level brain-like signal processing.
abstract: Sensory information processing and cognition in brains are modeled using dynamic systems theory. The brain's dynamic state is described by a trajectory evolving in a high-dimensional state space. We introduce a hierarchy of random cellular automata as the mathematical tools to describe the spatio-temporal dynamics of the cortex. The corresponding brain model is called neuropercolation which has distinct advantages compared to traditional models using differential equations, especially in describing spatio-temporal discontinuities in the form of phase transitions. Phase transitions demarcate singularities in brain operations at critical conditions, which are viewed as hallmarks of higher cognition and awareness experience. The introduced Monte-Carlo simulations obtained by parallel computing point to the importance of computer implementations using very large-scale integration (VLSI) and analog platforms.
authors.affiliation: Department of Mathematical Sciences, University of Memphis, Memphis, TN 38152, USA. rkozma@memphis.edu
authors.forename: Robert
authors.initials: R
authors.lastname: Kozma
authors.forename: Marko
authors.initials: M
authors.lastname: Puljic
mesh_terms: D001921:Brain / Q000166:cytology* / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics
keywords: Freeman K-sets; Memristor; Neurodynamics; Neuropercolation; Perceptual information processing; Phase transition; Random cellular automata; Synchronization