The computer and the brain
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
Intentional dynamic systems: Fundamental concepts and applications: Introduction
International Journal of Intelligent Systems - Intentional Dynamic Systems—Foundations, Modeling, and Robot Implementation
Large deviations for mean field models of probabilistic cellular automata
Random Structures & Algorithms
Computational Aspects of Cognition and Consciousness in Intelligent Devices
IEEE Computational Intelligence Magazine
Habituation in the KIII olfactory model with chemical sensor arrays
IEEE Transactions on Neural Networks
Dynamical analysis of neural oscillators in an olfactory cortex model
IEEE Transactions on Neural Networks
Advances in Neuromorphic Memristor Science and Applications
Advances in Neuromorphic Memristor Science and Applications
| Hi-index | 0.00 |
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.