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Bioinformation processing: a primer on computational cognitive science
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...
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Lenguaje: | eng |
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Springer
2016
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-981-287-871-7 http://cds.cern.ch/record/2137884 |
_version_ | 1780950022552027136 |
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author | Peterson, James K |
author_facet | Peterson, James K |
author_sort | Peterson, James K |
collection | CERN |
description | 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. |
id | cern-2137884 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2016 |
publisher | Springer |
record_format | invenio |
spelling | cern-21378842021-04-21T19:45:54Zdoi:10.1007/978-981-287-871-7http://cds.cern.ch/record/2137884engPeterson, James KBioinformation processing: a primer on computational cognitive scienceEngineeringThis 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.Springeroai:cds.cern.ch:21378842016 |
spellingShingle | Engineering Peterson, James K Bioinformation processing: a primer on computational cognitive science |
title | Bioinformation processing: a primer on computational cognitive science |
title_full | Bioinformation processing: a primer on computational cognitive science |
title_fullStr | Bioinformation processing: a primer on computational cognitive science |
title_full_unstemmed | Bioinformation processing: a primer on computational cognitive science |
title_short | Bioinformation processing: a primer on computational cognitive science |
title_sort | bioinformation processing: a primer on computational cognitive science |
topic | Engineering |
url | https://dx.doi.org/10.1007/978-981-287-871-7 http://cds.cern.ch/record/2137884 |
work_keys_str_mv | AT petersonjamesk bioinformationprocessingaprimeroncomputationalcognitivescience |