BioInformation Processing: A Primer on Computational Cognitive Science: Cognitive Science and Technology
Autor James K. Petersonen Limba Engleză Hardback – 19 feb 2016
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Specificații
ISBN-13: 9789812878694
ISBN-10: 9812878696
Pagini: 500
Ilustrații: XXXV, 570 p. 165 illus. in color.
Dimensiuni: 155 x 235 x 33 mm
Greutate: 1.02 kg
Ediția:1st ed. 2016
Editura: Springer Nature Singapore
Colecția Springer
Seria Cognitive Science and Technology
Locul publicării:Singapore, Singapore
ISBN-10: 9812878696
Pagini: 500
Ilustrații: XXXV, 570 p. 165 illus. in color.
Dimensiuni: 155 x 235 x 33 mm
Greutate: 1.02 kg
Ediția:1st ed. 2016
Editura: Springer Nature Singapore
Colecția Springer
Seria Cognitive Science and Technology
Locul publicării:Singapore, Singapore
Public țintă
ResearchCuprins
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.
Notă biografică
Dr. James Peterson is an Associate Professor in Mathematical Sciences and Biological Sciences at Clemson University, USA. His formal training is in mathematics but he has worked as an aerospace engineer and a software engineer also. He enjoys working on very hard problems that require multiple disciplines to make sense out of and he reads, studies and plays in cutting edge areas a lot as part of his interests.
Textul de pe ultima copertă
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.
Caracteristici
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