Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

742 Pages·2019·31.02 MB·English

by  Nikola K. Kasabov

Springer Series on Bio- and Neurosystems 7
Nikola K. Kasabov
Time-Space, Spiking
Neural Networks and
Brain-Inspired Artificial
Intelligence
Springer Series on Bio- and Neurosystems
Volume 7
Series editor
Nikola K. Kasabov, Auckland University of Technology, Auckland, New Zealand
The Springer Series on Bio- and Neurosystems publishes fundamental principles
and state-of-the-art research at the intersection of biology, neuroscience, informa-
tionprocessingandtheengineeringsciences.Theseriescoversgeneralinformatics
methods and techniques, together with their use to answer biological or medical
questions.Ofinterestarebothbasicsandnewdevelopmentsontraditionalmethods
such as machine learning, artificial neural networks, statistical methods, nonlinear
dynamics,informationprocessingmethods,andimageandsignalprocessing.New
findingsinbiologyandneuroscienceobtainedthroughinformaticsandengineering
methods,topicsinsystemsbiology,medicine,neuroscienceandecology,aswellas
engineering applications such as robotic rehabilitation, health information tech-
nologies, and many more, are also examined. The main target group includes
informaticians and engineers interested in biology, neuroscience and medicine, as
well as biologists and neuroscientists using computational and engineering tools.
Volumespublishedintheseriesincludemonographs,editedvolumes,andselected
conference proceedings. Books purposely devoted to supporting education at the
graduate and post-graduate levels in bio- and neuroinformatics, computational
biologyandneuroscience,systemsbiology,systemsneuroscienceandotherrelated
areas are of particular interest.
More information about this series at http://www.springer.com/series/15821
Nikola K. Kasabov
Time-Space, Spiking Neural
Networks and Brain-Inspired
fi
Arti cial Intelligence
123
NikolaK.Kasabov
KnowledgeEngineering andDiscovery
ResearchInstitute (KEDRI)
Auckland University of Technology
Auckland,New Zealand
ISSN 2520-8535 ISSN 2520-8543 (electronic)
SpringerSeries onBio- andNeurosystems
ISBN978-3-662-57713-4 ISBN978-3-662-57715-8 (eBook)
https://doi.org/10.1007/978-3-662-57715-8
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Time lives inside us and we live inside Time.
Vasil Levski-Apostola (1837–1873)
Bulgarian Educator and Revolutionary
To my mother Kapka Nikolova
Mankova-Kasabova (1920–2012) and my
father Kiril Ivanov Kasabov (1914–1996),
who gave me the light of life, and for those
who came earlier in time; to my family,
Diana, Kapka and Assia, who give me the
light oflove; andtothosewhowill comelater
in time; I hope they will enjoy the light of life
and the light of love as much as I do.
Foreword
Professor Furber is ICL Professor of Computer Engineering in the School of
Computer Science at the University of Manchester, UK. After completing his
education at the University of Cambridge (BA, MA, MMath, Ph.D.), he spent the
1980s at Acorn Computers, where he was a principal designer of the BBC Micro
and the ARM 32-bit RISC microprocessor. As of 2018, over 120 billion variants
of the ARM processor have been manufactured, powering much of the world’s
mobile computing and embedded systems. He pioneered the development of
SpiNNaker,aneuromorphiccomputerarchitecturethatenablestheimplementation
of massively parallel spiking neural network systems with a wide range of
applications.
The last decade has seen an explosion in the deployment of artificial neural
networks for machine learning applications ranging from consumer speech recog-
nition systems through to vision systems for autonomous vehicles. These artificial
neuralsystemsdifferfrombiologicalneuralsystemsinmanyimportantaspects,but
most notably in their use of neurons with continuously varying outputs where
biology predominantly uses spiking neurons—neurons that emit a pure
electro-chemical unit impulse in response to recognising an input pattern. The
continuous output of the artificial neuron can be thought of as representing the
ix
x Foreword
meanfiringrateofitsbiologicalequivalent,butinusingratesratherthanspikes,the
artificialnetworklosestheabilitytoaccessthedetailedspatio-temporalinformation
that can be conveyed in a time sequence of spikes. Biological systems can clearly
access this information, but how they use it effectively remains a mystery to
science.
Nik Kasabov has done as much as anyone to begin to unlock the secrets of the
biological spatio-temporal patterns of spikes, and in this book, he reveals what he
has learnt about those secrets and how he has applied that knowledge in exciting
new ways. This is deep knowledge, and if we can harness such knowledge in
brain-inspired AI systems, then the explosion in AI witnessed over the last decade
willlooklikeadampsquibincomparisonwithwhatistofollow.Thisbookisnot
just a record of past work, but also a guidebook for an exciting future!
Steve Furber
CBE, FRS, FREng
Computer Science Department
University of Manchester, UK
Preface
Everything exists and evolves within time–space and time–space is within every-
thing, from a molecule to the universe. Understanding the complex relationship
between time and space has been one of the biggest scientific challenges of all
times, including the understanding and modelling the time–space information
processes in the human brain and understanding life. This is the strive for deep
knowledge that has always been the main goal of the human race.
Now that an enormous amount of time–space data is available, science needs
new methods to deal with the complexity of such data across domain areas. Risk
mitigation strategies from health to civil defence often depend on simple models.
But recent advances in machine learning offer the intriguing possibility that dis-
astrous events, as diverse as strokes, earthquakes, financial market crises, or
degenerativebraindiseases,couldbepredictedearlyifthepatternshiddendeeplyin
the intricate and complex interactions between spatial and temporal components
could be understood. Although such interactions are manifested at different spatial
or temporal scales in different applications or domain areas, the same information-
processing principles may be applied.
A radically new approach to modelling such data and to obtaining deep
knowledgeisneededthatcouldenablethecreationoffasterandsignificantlybetter
machinelearning andpatternrecognitionsystems,offeringtherealistic prospect of
much more accurate and earlier event prediction, and a better understanding of
causal time–space relationships.
The term time–space coined in this book has two meanings:
– The problem space, where temporal processes evolve in time;
– The functional space of time, as it goes by.
This book looks at evolving processes in time–space. It talks about how deep
learningoftime–space data isachievedinthehuman brainandhowthisresultsin
deep knowledge,which istaken asinspiration todevelopmethods and systems for
deep learning and deep knowledge representation in spiking neural networks
(SNN).Andfurthermore,howthiscouldbeusedtodevelopanewtypeofartificial
intelligence(AI)systems,herecalledbrain-inspiredAI(BI-AI).Inturn,theseBI-AI
xi