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.
