This book bridges the communication gap between neuroscientists and
engineers through the unifying theme of correlation-based learning
Developing brain-style signal processing or machine learning
algorithms has attracted many sharp minds from a range of
disciplines. Now, coauthored by four researchers with varying
backgrounds in signal processing, neuroscience, psychology, and
computer science, Correlative Learning unifies the many
cross-fertilized ideas in computational neuroscience and signal
processing in a common language that will help engineers understand
and appreciate the human brain as a highly sophisticated biosystem
for building more intelligent machines.
First, the authors present the necessary neuroscience background
for engineers, and then go on to relate the common intrinsic
structures of the learning mechanisms of the brain to signal
processing, machine learning, kernel learning, complex-valued
domains, and the ALOPEX learning paradigm.
This correlation-based approach to building complex, reliable
(robust), and adaptive systems is vital for engineers, researchers,
and graduate students from various fields of science and
engineering. Figures, tables, worked examples, and case studies
illustrate how to use computational tools for either helping to
understand brain functions or fitting specific engineering
applications, and a comprehensive bibliography covering over 1,000
references from major publications is included for further
reading.
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