## Browse content

### Table of contents

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#### Chapter 1 - Calculus Ratiocinator

Pages 1-25 - Book chapterAbstract only
#### Chapter 2 - Most Likely Inference

Pages 27-58 - Book chapterAbstract only
#### Chapter 3 - Probability Learning and Memory

Pages 59-93 - Book chapterAbstract only
#### Chapter 4 - Causal Reasoning

Pages 95-123 - Book chapterAbstract only
#### Chapter 5 - Neural Calculus

Pages 125-144 - Book chapterAbstract only
#### Chapter 6 - Oscillating Neural Synchrony

Pages 145-174 - Book chapterAbstract only
#### Chapter 7 - Alzheimer's and Mind–Brain Problems

Pages 175-195 - Book chapterAbstract only
#### Chapter 8 - Let Us Calculate

Pages 197-209 - Book chapterNo access
#### Appendix

Pages 211-242 - Book chapterNo access
#### Notes and References

Pages 243-270 - Book chapterNo access
#### Index

Pages 271-280

## About the book

### Description

*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.

*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.

### Key Features

- Provides a high-level introduction and detailed reviews of the neural, statistical and machine learning knowledge base as a foundation for a new era of smarter machines
- Argues that smarter machine learning to handle both explanation and prediction without cognitive bias must have a foundation in cognitive neuroscience and must embody similar explicit and implicit learning principles that occur in the brain

- Provides a high-level introduction and detailed reviews of the neural, statistical and machine learning knowledge base as a foundation for a new era of smarter machines
- Argues that smarter machine learning to handle both explanation and prediction without cognitive bias must have a foundation in cognitive neuroscience and must embody similar explicit and implicit learning principles that occur in the brain

## Details

### ISBN

978-0-12-410407-5

### Language

English

### Published

2014

### Copyright

Copyright © 2014 Elsevier Inc. All rights reserved.

### Imprint

Academic Press