Overview
- Nominated as an outstanding PhD thesis by Heidelberg University, Germany
- Provides an excellent state-of-the-art overview of theoretical neuroscience
- An inspiration for newcomers to engage in this fascinating and future-oriented field of research
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Theses (Springer Theses)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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 transfercan 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.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Form Versus Function: Theory and Models for Neuronal Substrates
Authors: Mihai Alexandru Petrovici
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-319-39552-4
Publisher: Springer Cham
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-39551-7Published: 27 July 2016
Softcover ISBN: 978-3-319-81913-6Published: 31 May 2018
eBook ISBN: 978-3-319-39552-4Published: 19 July 2016
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XXVI, 374
Number of Illustrations: 49 b/w illustrations, 101 illustrations in colour
Topics: Numerical and Computational Physics, Simulation, Mathematical Models of Cognitive Processes and Neural Networks, Neurobiology, Neurosciences, Simulation and Modeling