東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Cybernetics 2.0 = a general theory o...

Widrow, Bernard.

Linked to FindBook

Google Book

Amazon

博客來

Cybernetics 2.0 = a general theory of adaptivity and homeostasis in the brain and in the body /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Cybernetics 2.0/ by Bernard Widrow.
Reminder of title:	a general theory of adaptivity and homeostasis in the brain and in the body /
Author:	Widrow, Bernard.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xxxv, 307 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Cybernetics - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-3-030-98140-2
ISBN:	9783030981402

Cybernetics 2.0 = a general theory of adaptivity and homeostasis in the brain and in the body /
Widrow, Bernard.

Cybernetics 2.0a general theory of adaptivity and homeostasis in the brain and in the body /[electronic resource] :by Bernard Widrow. - Cham :Springer International Publishing :2023. - xxxv, 307 p. :ill. (some col.), digital ;24 cm. - Springer series on bio- and neurosystems,v. 142520-8543 ;. - Springer series on bio- and neurosystems ;v. 14..

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.

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.

ISBN: 9783030981402

Standard No.: 10.1007/978-3-030-98140-2doiSubjects--Topical Terms:

3626218
Cybernetics
--Mathematical models.

LC Class. No.: Q310

Dewey Class. No.: 003.5

Cybernetics 2.0 = a general theory of adaptivity and homeostasis in the brain and in the body /
LDR:03607nmm a2200337 a 4500 001 2314634
003 DE-He213
005 20221015112621.0
006 m d
007 cr nn 008maaau
008 230902s2023 sz s 0 eng d
020 $a 9783030981402 $q (electronic bk.)
020 $a 9783030981396 $q (paper)
024 7 $a 10.1007/978-3-030-98140-2 $2 doi
035 $a 978-3-030-98140-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q310
072 7 $a MQW $2 bicssc
072 7 $a TEC059000 $2 bisacsh
072 7 $a MQW $2 thema
082 0 4 $a 003.5 $2 23
090 $a Q310 $b .W641 2023
100 1 $a Widrow, Bernard. $3 3626216
245 1 0 $a Cybernetics 2.0 $h [electronic resource] : $b a general theory of adaptivity and homeostasis in the brain and in the body / $c by Bernard Widrow.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xxxv, 307 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer series on bio- and neurosystems, $x 2520-8543 ; $v v. 14
505 0 $a 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.
520 $a 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.
650 0 $a Cybernetics $x Mathematical models. $3 3626218
650 0 $a Neurotechnology (Bioengineering) $x Mathematical models. $3 3626219
650 1 4 $a Bioinspired Technologies. $3 3594575
650 2 4 $a Complex Systems. $3 1566441
650 2 4 $a Machine Learning. $3 3382522
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer series on bio- and neurosystems ; $v v. 14. $3 3626217
856 4 0 $u https://doi.org/10.1007/978-3-030-98140-2
950 $a Engineering (SpringerNature-11647)