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BioInformation processing [electronic resource] : a primer on computational cognitive science / James K. Peterson.

Peterson, James K. (James Kent), author.
2016
QH324.2
Available Online
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Title
BioInformation processing [electronic resource] : a primer on computational cognitive science / James K. Peterson.
Author
Peterson, James K. (James Kent), author.
ISBN
9789812878717 (electronic book)
9812878718 (electronic book)
9789812878694
DOI
https://doi.org/10.1007/978-981-287-871-7
Published
Singapore : Springer, 2016.
Language
English
Description
1 online resource (xxxv, 570 pages) : color illustrations.
Item Number
10.1007/978-981-287-871-7 doi
Call Number
QH324.2
Dewey Decimal Classification
570.285
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 18, 2016).
Series
Cognitive science and technology.
Available in Other Form
Print version: 9789812878694
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Part I: Introductory matter
BioInformation processing
Part II: Diffusion models
The diffusion equation
Integral transforms
The time dependent cable solution
Part III: Neural systems
Mammalian neural structure
Abstracting principles of computation
Second messenger diffusion pathways
Second messenger models
The abstract neuron model
Part IV: Models of emotion and cognition
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
Part V: Simple abstract neurons
Matrix feed forward networks
Chained feed forward architectures
Part VI: Graph based modeling in Matlab
Graph models
Address based graphs
Building brain models
Part VII: Models of cognition dysfunction
Models of cognitive dysfunction
Part VIII: Conclusions
Conclusions
Part IX: Background reading
Background reading.

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