Kernel Methods for Pattern Analysis
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Human cognition performs granulation of the seemingly homogeneous temporal sequences of perceptual experiences into meaningful and comprehensible chunks of fuzzy concepts and behaviors. These knowledge granules are stored and consequently accessed during action selection and decisions. A dynamical approach is presented here to interpret experimental findings using K (Katchalsky) models. In the K model, meaningful knowledge is repetitiously created and processed in the form of sequences of oscillatory patterns of neural activity distributed across space and time. These patterns are not rigid but flexible and intermittent; soon after they arise through phase transitions, they dissipate. Computational implementations demonstrate the operation of the model based on the principles of intentional brain dynamics.