Cybernetics 2.0 : a general theory of adaptivity and homeostasis in the brain and in the body / Bernard Widrow.
Widrow, Bernard, 1929- author.
2023
Q310
Available Online

Linked e-resources

Linked Resource Online Access
Concurrent users Unlimited
Authorized users Authorized users
Document Delivery Supplied Can lend chapters, not whole ebooks

Details

Title Cybernetics 2.0 : a general theory of adaptivity and homeostasis in the brain and in the body / Bernard Widrow.
Author Widrow, Bernard, 1929- author.
ISBN 9783030981402 (electronic bk.)
3030981401 (electronic bk.)
9783030981396 (print)
3030981398
DOI https://doi.org/10.1007/978-3-030-98140-2
Published Cham, Switzerland : Springer, [2023]
Language English
Description 1 online resource (xxxv, 307 pages) : illustrations (some color).
Item Number 10.1007/978-3-030-98140-2 doi
Call Number Q310
Dewey Decimal Classification 003/.5
Summary This book takes the notions of adaptivity and learning from the realm of engineering into the realm of biology and natural processes. It introduces a Hebbian-LMS algorithm, an integration of unsupervised Hebbian learning and supervised LMS learning in neural networks, as a mathematical representation of a general theory for synaptic learning in the brain, and adaptation and functional control of homeostasis in living systems. Written in a language that is able to address students and scientists with different backgrounds, this book accompanies readers on a unique journey through various homeostatic processes in living organisms, such as body temperature control and synaptic plasticity, explaining how the Hebbian-LMS algorithm can help understand them, and suggesting some open questions for future research. It also analyses cell signalling pathways from an unusual perspective, where hormones and hormone receptors are shown to be regulated via the principles of the Hebbian-LMS algorithm. It further discusses addiction and pain, and various kinds of mood disorders alike, showing how they can be modelled with the Hebbian-LMS algorithm. For the first time, the Hebbian-LMS algorithm, which has been derived from a combination of Hebbian theory from the neuroscience field and the LMS algorithm from the engineering field of adaptive signal processing, becomes a potent model for understanding how biological regulation works. Thus, this book is breaking new ground in neuroscience by providing scientists with a general theory for how nature does control synaptic learning. It then goes beyond that, showing that the same principles apply to hormone-mediated regulation of physiological processes. In turn, the book tackles in more depth the concept of learning. It covers computer simulations and strategies for training neural networks with the Hebbian-LMS algorithm, demonstrating that the resulting algorithms are able to identify relationships between unknown input patterns. It shows how this can translate in useful ideas to understand human memory and design cognitive structures. All in all, this book offers an absolutely, unique, inspiring reading for biologists, physiologists, and engineers, paving the way for future studies on what we could call the nature's secret learning algorithm.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed October 19, 2022).
Series Springer series on bio- and neurosystems ; v. 14. 2520-8543
Available in Other Form Print version: 9783030981396
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
Introduction
Hebbian Learning
The LMS Algorithm
The Hebbian-LMS Algorithm
Inside the membrane; The percent Hebbian-LMS algorithm
Synaptic scaling and homeostasis
Synaptic plasticity
Addiction.

Browse Subjects

Show more subjects...

Statistics

from
to
Export