Calculus of Thought book cover

Calculus of Thought

Neuromorphic Logistic Regression in Cognitive Machines

Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines is a must-read for all scientists about a very simple computation method designed to simulate big-data neural processing. This book is inspired by the Calculus Ratiocinator idea of Gottfried Leibniz, which is that machine computation should be developed to simulate human cognitive processes, thus avoiding problematic subjective bias in analytic solutions to practical and scientific problems.

The reduced error logistic regression (RELR) method is proposed as such a "Calculus of Thought." This book reviews how RELR's completely automated processing may parallel important aspects of explicit and implicit learning in neural processes. It emphasizes the fact that RELR is really just a simple adjustment to already widely used logistic regression, along with RELR's new applications that go well beyond standard logistic regression in prediction and explanation. Readers will learn how RELR solves some of the most basic problems in today’s big and small data related to high dimensionality, multi-colinearity, and cognitive bias in capricious outcomes commonly involving human behavior.


Data Mining, Applied Math and Statistics, Modeling, and Cognitive Neuroscience

Hardbound, 272 Pages

Published: November 2013

Imprint: Academic Press

ISBN: 978-0-12-410407-5


  • Preface: A Personal Perspective
    1. Calculus Ratiocinator
    2. Most Likely Inference
    3. Conditional Probability Learning
    4. Causal Reasoning
    5. Neural Calculus
    6. Oscillating Neural Synchrony
    7. Neural Natural Selection and Alzheimer’s Disease
    8. Let Us Calculate
    Appendix One: The RELR Formulation
    Appendix Two: The 2004 Election Weekend Survey Model


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