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 and thus avoid problematic subjective bias in analytic solutions to practical and scientific problems. The Reduced Error Logistic Regression (RELR) method is proposed to be such a Calculus of Thought, as this book reviews how RELR’s completely automated processing may parallel important aspects of both explicit and implicit learning in neural processes. The fact that RELR is really just a simple adjustment to already widely used logistic regression is emphasized, along with RELR’s new applications that go well beyond standard logistic regression in both prediction and explanation. Particular attention is given to how RELR solves some of the most basic problems in today’s big and small data related to high dimensionality, multicollinearity and cognitive bias in capricious outcomes often involving human behavior.

Key Features

• Provides a high level introduction with detailed reviews of the neural, statistical and machine learning knowledge base as a foundation for this 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 embody similar explicit and implicit learning principles that occur in the brain.

Daniel M. Rice, Ph.D. is the Principal and Senior Scientist and founder of Rice Analytics in St Louis, Missouri. He is both a cognitive neuroscientist and statistician and has been practicing advanced analytic science in either medical, academic or industry settings for his entire career. 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 academic and industry publications in cognitive neuroscience, statistics, machine learning, and analytics. In the early 1990’s, he was the lead author on two papers that related explicit memory performance and temporal lobe brain measures to make the initial discovery and claim that Alzheimer’s disease must have an average preclinical causal period of at least 10 years. Since that time, he has worked to develop automated machine learning methods that simulate basic explicit and implicit cognitive neural processes and allow most likely solutions that avoid traditional problems related to error and bias.

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.