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Form Versus Function: Theory and Models for Neuronal Substrates (Paperback, Softcover reprint of the original 1st ed. 2016)
Loot Price: R3,663
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Form Versus Function: Theory and Models for Neuronal Substrates (Paperback, Softcover reprint of the original 1st ed. 2016)
Series: Springer Theses
Expected to ship within 18 - 22 working days
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This thesis addresses one of the most fundamental challenges for
modern science: how can the brain as a network of neurons process
information, how can it create and store internal models of our
world, and how can it infer conclusions from ambiguous data? The
author addresses these questions with the rigorous language of
mathematics and theoretical physics, an approach that requires a
high degree of abstraction to transfer results of wet lab biology
to formal models. The thesis starts with an in-depth description of
the state-of-the-art in theoretical neuroscience, which it
subsequently uses as a basis to develop several new and original
ideas. Throughout the text, the author connects the form and
function of neuronal networks. This is done in order to achieve
functional performance of biological brains by transferring their
form to synthetic electronics substrates, an approach referred to
as neuromorphic computing. The obvious aspect that this transfer
can never be perfect but necessarily leads to performance
differences is substantiated and explored in detail. The author
also introduces a novel interpretation of the firing activity of
neurons. He proposes a probabilistic interpretation of this
activity and shows by means of formal derivations that stochastic
neurons can sample from internally stored probability
distributions. This is corroborated by the author's recent
findings, which confirm that biological features like the high
conductance state of networks enable this mechanism. The author
goes on to show that neural sampling can be implemented on
synthetic neuromorphic circuits, paving the way for future
applications in machine learning and cognitive computing, for
example as energy-efficient implementations of deep learning
networks. The thesis offers an essential resource for newcomers to
the field and an inspiration for scientists working in theoretical
neuroscience and the future of computing.
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