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El. knyga: Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines

3.00/5 (3 ratings by Goodreads)
(Principal and Senior Scientist and founder of Rice Analytics, St Louis, MO, USA)
  • Formatas: EPUB+DRM
  • Išleidimo metai: 15-Oct-2013
  • Leidėjas: Academic Press Inc
  • Kalba: eng
  • ISBN-13: 9780124104525
Kitos knygos pagal šią temą:
Calculus of Thought: Neuromorphic Logistic Regression in Cognitive MachinesDidesnis paveikslėlis
  • Formatas: EPUB+DRM
  • Išleidimo metai: 15-Oct-2013
  • Leidėjas: Academic Press Inc
  • Kalba: eng
  • ISBN-13: 9780124104525
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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."

Drawing on his research over the past 30 years, Rice argues that cognitive machines will need to be neuromorphic, that is, based upon neuroscience, in order to simulate aspects of human cognition. He sets out the most fundamental and important concepts in modern cognitive neuroscience, including neural dynamics, implicit and explicit learning, neural synchrony, Hebbian spike-timing dependent plasticity, and neural Darwinism. He places these concepts in relationship to reduced error logistic regression (RELR) computation mechanisms to provide an introductory overview for professionals interesting in neuromorphic cognitive machines. Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)

Recenzijos

"Rice argues that cognitive machines will need to be neuromorphic, that is, based upon neuroscience, in order to simulate aspects of human cognition. He sets out the most fundamental and important concepts in modern cognitive neuroscience, including neural dynamics, implicit and explicit learning, neural synchrony, Hebbian spike-timing dependent plasticity, and neural Darwinism." --ProtoView.com, February 2014

Daugiau informacijos

A must read for all scientists about a very simple computation method designed to simulate big data neural processing
Preface ix
Pt. 1 Calculus Ratiocinator 1(26)
1 A Fundamental Problem with the Widely Used Methods
4(5)
2 Ensemble Models and Cognitive Processing in Playing Jeopardy
9(2)
3 The Brain's Explicit and Implicit Learning
11(5)
4 Two Distinct Modeling Cultures and Machine Intelligence
16(3)
5 Logistic Regression and the Calculus Ratiocinator Problem
19(8)
Pt. 2 Most Likely Inference 27(32)
1 The Jaynes Maximum Entropy Principle
28(4)
2 Maximum Entropy and Standard Maximum Likelihood Logistic Regression
32(4)
3 Discrete Choice, Logit Error, and Correlated Observations
36(5)
4 RELR and the Logit Error
41(15)
5 RELR and the Jaynes Principle
56(3)
Pt. 3 Probability Learning and Memory 59(36)
1 Bayesian Online Learning and Memory
60(9)
2 Most Probable Features
69(4)
3 Implicit RELR
73(10)
4 Explicit RELR
83(12)
Pt. 4 Causal Reasoning 95(30)
1 Propensity Score Matching
97(5)
2 RELR's Outcome Score Matching
102(5)
3 An Example of RELR's Causal Reasoning
107(7)
4 Comparison to Other Bayesian and Causal Methods
114(11)
Pt. 5 Neural Calculus 125(20)
1 RELR as a Neural Computational Model
126(4)
2 RELR and Neural Dynamics
130(4)
3 Small Samples in Neural Learning
134(3)
4 What about Artificial Neural Networks?
137(8)
Pt. 6 Oscillating Neural Synchrony 145(30)
1 The EEG and Neural Synchrony
147(3)
2 Neural Synchrony, Parsimony, and Grandmother Cells
150(1)
3 Gestalt Prognanz and Oscillating Neural Synchrony
151(10)
4 RELR and Spike-Timing-Dependent Plasticity
161(2)
5 Attention and Neural Synchrony
163(3)
6 Metrical Rhythm in Oscillating Neural Synchrony
166(5)
7 Higher Frequency Gamma Oscillations
171(4)
Pt. 7 Alzheimer's and Mind–Brain Problems 175(22)
1 Neuroplasticity Selection in Development and Aging
176(3)
2 Brain and Cognitive Changes in Very Early Alzheimer's Disease
179(4)
3 A RELR Model of Recent Episodic and Semantic Memory
183(2)
4 What Causes the Medial Temporal Lobe Disturbance in Early Alzheimer's?
185(6)
5 The Mind–Brain Problem
191(6)
Pt. 8 Let Us Calculate 197(14)
1 Human Decision Bias and the Calculus Ratiocinator
200(2)
2 When the Experts are Wrong
202(3)
3 When Predictive Models Crash
205(2)
4 The Promise of Cognitive Machines
207(4)
Appendix 211(32)
Al RELR Maximum Entropy Formulation
211(12)
A2 Derivation of RELR Logit from Errors-in-Variables Considerations
223(1)
A3 Methodology for Pew 2004 Election Weekend Model Study
224(2)
A4 Derivation of Posterior Probabilities in RELR's Sequential Online Learning
226(3)
A5 Chain Rule Derivation of Explicit RELR Feature Importance
229(1)
A6 Further Details on the Explicit RELR Low Birth Weight Model in
Chapter 3
230(5)
A7 Zero Intercepts in Perfectly Balanced Stratified Samples
235(2)
A8 Detailed Steps in RELR's Causal Machine Learning Method
237(6)
Notes and References 243(28)
Index 271
Daniel M. Rice is Principal and Senior Scientist of Rice Analytics. He founded the business in early 1996 as a sole proprietorship, but it was incorporated into its current structure in 2006. Prior to 1996, he was an assistant professor at the University of California-Irvine and the University of Southern California. Dan has almost 25 years of research project and advanced statistical modeling experience for major organizations that include the National Institute on Aging, Eli Lilly, Anheuser-Busch, Sears Portrait Studios, Hewlett-Packard, UBS, and Bank of America. He has a Ph.D. from the University of New Hampshire in Cognitive Neuroscience and Postdoctoral training in Applied Statistics from the University of California-Irvine. Dan is a previous recipient of an Individual National Research Service Award from the National Institutes of Health and is author of more than 20 publications, many of which are in conference proceedings and peer-reviewed journals in cognitive neuroscience and statistics.
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