Authors:
- Nominated as an outstanding Ph.D. thesis by the University of Sheffield
- Reformulates, for the first time, the encoding performed by the integrate-and-fire neuron model as a problem of uniform sampling of an auxiliary function on a set of input independent time points
- Proposes two methodologies for identifying [Nonlinear Filter]-[Ideal IF] and [Linear Filter]-[Leaky IF] circuits
- Developes for the first time, a direct representation between the input and output of a linear filter, both encoded with an integrate-and-fire neuron model
- Includes supplementary material: sn.pub/extras
- 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
About this book
This work is motivated by the ongoing open question of how information in the outside world is represented and processed by the brain. Consequently, several novel methods are developed.
A new mathematical formulation is proposed for the encoding and decoding of analog signals using integrate-and-fire neuron models. Based on this formulation, a novel algorithm, significantly faster than the state-of-the-art method, is proposed for reconstructing the input of the neuron.
Two new identification methods are proposed for neural circuits comprising a filter in series with a spiking neuron model. These methods reduce the number of assumptions made by the state-of-the-art identification framework, allowing for a wider range of models of sensory processing circuits to be inferred directly from input-output observations.
A third contribution is an algorithm that computes the spike time sequence generated by an integrate-and-fire neuron model in response to the output of alinear filter, given the input of the filter encoded with the same neuron model.Authors and Affiliations
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Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom
Dorian Florescu
About the author
Dr Dorian Florescu is currently a Postdoctoral Research Fellow in the Department of Automatic Control and Systems Engineering at the University of Sheffield, working on the ‘Digital Fruit Fly Brain’ project, funded jointly by BBSRC and the National Science Foundation.
He was awarded the BEng degree in Systems Engineering from the Technical University of Iasi, Romania, in 2011 and the PhD degree in Automatic Control & Systems Engineering from the University of Sheffield
Bibliographic Information
Book Title: Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits
Authors: Dorian Florescu
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-319-57081-5
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-57080-8Published: 03 May 2017
Softcover ISBN: 978-3-319-86072-5Published: 25 July 2018
eBook ISBN: 978-3-319-57081-5Published: 24 April 2017
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XIV, 139
Number of Illustrations: 15 b/w illustrations, 27 illustrations in colour
Topics: Signal, Image and Speech Processing, Mathematical Models of Cognitive Processes and Neural Networks, Neurosciences, Systems Theory, Control, Circuits and Systems