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Neuronal Noise covers many aspects of noise in neurons, with an
emphasis on synaptic noise. It includes a combination of
experimental, theoretical and computational results to show how
noise is inherent to neuronal activity, and how noise can be
important for neuronal computations. The goal of this book is to
provide students and young researchers with an overview of the
important methods and concepts that have emerged from research in
this area. It also provides the specialist with a summary of the
large body of sometimes contrasting experimental data, and the
different theories proposed to explore the computational power that
various forms of "noise" can confer to neurons.
Since the early eighteenth century, the theory of networks and
graphs has matured into an indispensable tool for describing
countless real-world phenomena. However, the study of large-scale
features of a network often requires unrealistic limits, such as
taking the network size to infinity or assuming a continuum. These
asymptotic and analytic approaches can significantly diverge from
real or simulated networks when applied at the finite scales of
real-world applications. This book offers an approach to overcoming
these limitations by introducing operator graph theory, an exact,
non-asymptotic set of tools combining graph theory with operator
calculus. The book is intended for mathematicians, physicists, and
other scientists interested in discrete finite systems and their
graph-theoretical description, and in delineating the abstract
algebraic structures that characterise such systems. All the
necessary background on graph theory and operator calculus is
included for readers to understand the potential applications of
operator graph theory.
Neuronal Noise combines experimental, theoretical and computational
results to show how noise is inherent to neuronal activity, and how
noise can be important for neuronal computations. The book covers
many aspects of noise in neurons, with an emphasis on the largest
source of noise: synaptic noise. It provides students and young
researchers with an overview of the important methods and concepts
that have emerged from research in this area. It also provides the
specialist with a summary of the large body of sometimes
contrasting experimental data, and different theories proposed to
explore the computational power that various forms of "noise" can
confer to neurons.
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