BioInformation Processing
A Primer on Computational Cognitive Science
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Beschreibung
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.
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.
Uses the principles of computation in neural systems to develop approximations for neural computation Proposes network models of computation based on models of emotion arising from musical and painting data Uses graphical computational tools for exploring neural models Includes supplementary material: sn.pub/extras
Autor*in
James K. Peterson
Themen in »BioInformation Processing«
Abstract Computation BioInformation Processing Brain Models Chained Feed Forward Networks Cognitive Dysfunction Models Lagged Chains Matrix Feed Forward Networks Neural Structure