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BioInformation Processing [electronic resource] : A Primer on Computational Cognitive Science / by James K. Peterson.

By: Peterson, James K [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Cognitive Science and Technology: Publisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2016Edition: 1st ed. 2016.Description: XXXV, 570 p. 165 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789812878717.Subject(s): Computational intelligence | Mathematical physics | Neural networks (Computer science)  | Artificial intelligence | Image processing—Digital techniques | Computer vision | Computational Intelligence | Theoretical, Mathematical and Computational Physics | Mathematical Models of Cognitive Processes and Neural Networks | Artificial Intelligence | Computer Imaging, Vision, Pattern Recognition and GraphicsAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
BioInformation Processing -- The Diffusion Equation -- Integral Transforms -- The Time Dependent Cable Solution -- Mammalian Neural Structure -- Abstracting Principles of Computation -- Abstracting Principles of Computation -- Second Messenger Diffusion 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.
In: Springer Nature eBookSummary: 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.
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BioInformation Processing -- The Diffusion Equation -- Integral Transforms -- The Time Dependent Cable Solution -- Mammalian Neural Structure -- Abstracting Principles of Computation -- Abstracting Principles of Computation -- Second Messenger Diffusion 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.

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.

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