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BioInformation Processing

A Primer on Computational Cognitive Science

  • James K.┬áPeterson
Book

Part of the Cognitive Science and Technology book series (CSAT)

Table of contents

  1. Front Matter
    Pages i-xxxv
  2. Introductory Matter

    1. Front Matter
      Pages 1-1
    2. James K. Peterson
      Pages 3-15
  3. Diffusion Models

    1. Front Matter
      Pages 17-17
    2. James K. Peterson
      Pages 19-37
    3. James K. Peterson
      Pages 39-44
    4. James K. Peterson
      Pages 45-58
  4. Neural Systems

    1. Front Matter
      Pages 59-59
    2. James K. Peterson
      Pages 61-82
    3. James K. Peterson
      Pages 83-105
    4. James K. Peterson
      Pages 107-116
    5. James K. Peterson
      Pages 117-136
    6. James K. Peterson
      Pages 137-171
  5. Models of Emotion and Cognition

    1. Front Matter
      Pages 173-173
    2. James K. Peterson
      Pages 175-182
    3. James K. Peterson
      Pages 183-204
    4. James K. Peterson
      Pages 227-250
    5. James K. Peterson
      Pages 251-276
    6. James K. Peterson
      Pages 277-284
  6. Simple Abstract Neurons

    1. Front Matter
      Pages 285-285
    2. James K. Peterson
      Pages 287-314
    3. James K. Peterson
      Pages 315-330
  7. Graph Based Modeling In Matlab

    1. Front Matter
      Pages 331-331
    2. James K. Peterson
      Pages 333-415
    3. James K. Peterson
      Pages 417-460
    4. James K. Peterson
      Pages 461-491
  8. Models of Cognition Dysfunction

    1. Front Matter
      Pages 493-493
    2. James K. Peterson
      Pages 495-515
  9. Conclusions

    1. Front Matter
      Pages 517-517
    2. James K. Peterson
      Pages 519-522
  10. Background Reading

    1. Front Matter
      Pages 523-523
    2. James K. Peterson
      Pages 525-537
  11. Back Matter
    Pages 539-570

About this book

Introduction

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.

Keywords

Abstract Computation BioInformation Processing Brain Models Chained Feed Forward Networks Cognitive Dysfunction Models Lagged Chains Matrix Feed Forward Networks Neural Structure

Authors and affiliations

  • James K.┬áPeterson
    • 1
  1. 1.Dept of Mathematical SciencesClemson UniversityClemsonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-287-871-7
  • Copyright Information Springer Science+Business Media Singapore 2016
  • Publisher Name Springer, Singapore
  • eBook Packages Engineering
  • Print ISBN 978-981-287-869-4
  • Online ISBN 978-981-287-871-7
  • Series Print ISSN 2195-3988
  • Series Online ISSN 2195-3996
  • Buy this book on publisher's site