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This book shows how mathematics, computer science and science can be usefully and seamlessly intertwined. It begins with a general model of cognitive processes in a network of computational nodes, such as neurons, using a variety of tools from mathematics, computational science and neurobiology. It then moves on to solve the diffusion model from a low-level random walk point of view. It also demonstrates how this idea can be used in a new approach to solving the cable equation, in order to better understand the neural computation approximations. It introduces specialized data for emotional content, which allows a brain model to be built using MatLab tools, and also highlights a simple model of cognitive dysfunction.

BioInformation Processing: A Primer on Computational Cognitive Science

BioInformation Processing.- The Di usion Equation.- Integral Transforms.- The Time Dependent Cable Solution.- Mammalian Neural Structure.- Abstracting Principles of Computation.- Abstracting Principles of Computation.- Second Messenger Di usion Pathways.- The Abstract Neuron Model.- Emotional Models.- Generation of Music Data: J. Peterson and L. Dzuris.- Generation of Painting Data: J. Peterson, L. Dzuris and Q. Peterson.- Modeling Compositional Design.- Networks Of Excitable Neurons.- Training The Model.- Matrix Feed Forward Networks.- Chained Feed Forward Architectures.- Graph Models.- Address Based Graphs.- Building Brain Models.- Models of Cognitive Dysfunction.- Conclusions.- Background Reading.