Calculus of thought : neuromorphic logistic regression in cognitive machines / Daniel M. Rice.

Format:

Book

Author/Creator:

Rice, Daniel M.

Series:

Gale eBooks

Language:

English

Subjects (All):

Computational neuroscience.

Cognitive science--Mathematical models.

Cognitive science.

Physical Description:

1 online resource (xiv, 280 pages) : illustrations (some color)

Edition:

1st edition

Other Title:

Neuromorphic logistic regression in cognitive machines

Place of Publication:

Waltham, MA : Academic Press, 2014.

Language Note:

English

System Details:

text file

Summary:

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 re

Contents:

Front Cover; Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines; Copyright; Dedication; Contents; Preface; Chapter 1 - Calculus Ratiocinator; 1 A FUNDAMENTAL PROBLEM WITH THE WIDELY USED METHODS; 2 ENSEMBLE MODELS AND COGNITIVE PROCESSING IN PLAYING JEOPARDY; 3 THE BRAIN'S EXPLICIT AND IMPLICIT LEARNING; 4 TWO DISTINCT MODELING CULTURES AND MACHINE INTELLIGENCE; 5 LOGISTIC REGRESSION AND THE CALCULUS RATIOCINATOR PROBLEM; Chapter 2 - Most Likely Inference; 1 THE JAYNES MAXIMUM ENTROPY PRINCIPLE; 2 MAXIMUM ENTROPY AND STANDARD MAXIMUM LIKELIHOOD LOGISTIC REGRESSION

3 DISCRETE CHOICE, LOGIT ERROR, AND CORRELATED OBSERVATIONS4 RELR AND THE LOGIT ERROR; 5 RELR AND THE JAYNES PRINCIPLE; Chapter 3 - Probability Learning and Memory; 1 BAYESIAN ONLINE LEARNING AND MEMORY; 2 MOST PROBABLE FEATURES; 3 IMPLICIT RELR; 4 EXPLICIT RELR; Chapter 4 - Causal Reasoning; 1 PROPENSITY SCORE MATCHING; 2 RELR'S OUTCOME SCORE MATCHING; 3 AN EXAMPLE OF RELR'S CAUSAL REASONING; 4 COMPARISON TO OTHER BAYESIAN AND CAUSAL METHODS; Chapter 5 - Neural Calculus; 1 RELR AS A NEURAL COMPUTATIONAL MODEL; 2 RELR AND NEURAL DYNAMICS; 3 SMALL SAMPLES IN NEURAL LEARNING

4 WHAT ABOUT ARTIFICIAL NEURAL NETWORKS?Chapter 6 - Oscillating Neural Synchrony; 1 THE EEG AND NEURAL SYNCHRONY; 2 NEURAL SYNCHRONY, PARSIMONY, AND GRANDMOTHER CELLS; 3 GESTALT PRAGNANZ AND OSCILLATING NEURAL SYNCHRONY; 4 RELR AND SPIKE-TIMING-DEPENDENT PLASTICITY; 5 ATTENTION AND NEURAL SYNCHRONY; 6 METRICAL RHYTHM IN OSCILLATING NEURAL SYNCHRONY; 7 HIGHER FREQUENCY GAMMA OSCILLATIONS; Chapter 7 - Alzheimer's and Mind-Brain Problems; 1 NEUROPLASTICITY SELECTION IN DEVELOPMENT AND AGING; 2 BRAIN AND COGNITIVE CHANGES IN VERY EARLY ALZHEIMER'S DISEASE

3 A RELR MODEL OF RECENT EPISODIC AND SEMANTIC MEMORY4 WHAT CAUSES THE MEDIAL TEMPORAL LOBE DISTURBANCE IN EARLY ALZHEIMER'S?; 5 THE MIND-BRAIN PROBLEM; Chapter 8 - Let Us Calculate; 1 HUMAN DECISION BIAS AND THE CALCULUS RATIOCINATOR; 2 WHEN THE EXPERTS ARE WRONG; 3 WHEN PREDICTIVE MODELS CRASH; 4 THE PROMISE OF COGNITIVE MACHINES; APPENDIX; NOTES AND REFERENCES; INDEX

Notes:

Description based upon print version of record

Includes bibliographical references and index

Description based on online resource; title from PDF title page (ebrary, viewed November 14, 2013)

ISBN:

9780124104525

0124104525

OCLC:

868231735