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Cybernetics 2.0 - A General Theory of Adaptivity and Homeostasis in the Brain and in the Body (Hardcover, 1st ed. 2023)
Loot Price: R3,006
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Cybernetics 2.0 - A General Theory of Adaptivity and Homeostasis in the Brain and in the Body (Hardcover, 1st ed. 2023)
Series: Springer Series on Bio- and Neurosystems, 14
Expected to ship within 10 - 15 working days
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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.
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