Reconstruction, identification and implementation methods for spiking neural circuits

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-fir...

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Bibliographic Details
Main Author: Florescu, Dorian, (Author)
Format: eBook
Language: English
Published: Cham, Switzerland : Springer, 2017.
Series: Springer theses.
Subjects:
Neural circuitry.
elektronické knihy
electronic books
ISBN: 9783319570815
9783319570808
Physical Description: 1 online resource (xiv, 139 pages) : illustrations (some color)

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024 7 |a 10.1007/978-3-319-57081-5  |2 doi 
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100 1 |a Florescu, Dorian,  |e author. 
245 1 0 |a Reconstruction, identification and implementation methods for spiking neural circuits /  |c Dorian Florescu. 
264 1 |a Cham, Switzerland :  |b Springer,  |c 2017. 
300 |a 1 online resource (xiv, 139 pages) :  |b illustrations (some color) 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
490 1 |a Springer theses 
500 |a "Doctoral thesis accepted by the University of Sheffield, Sheffield, UK." 
505 0 |a Supervisor's Foreword; Acknowledgements; Contents; Acronyms; 1 Introduction; 1.1 Background; 1.2 Motivation; 1.3 Overview of the Book; References; 2 Time Encoding and Decoding in Bandlimited and Shift-Invariant Spaces; 2.1 Introduction; 2.2 Nonuniform Sampling and Reconstruction of Bandlimited Functions; 2.3 Time Encoding and Decoding in Bandlimited Spaces; 2.3.1 The Ideal IF Neuron; 2.3.2 The Ideal IF Neuron with Refractory Period; 2.3.3 The Leaky IF Neuron; 2.3.4 The Leaky IF Neuron with Random Threshold; 2.3.5 The Hodgkin-Huxley Neuron; 2.3.6 The Asynchronous Sigma-Delta Modulator. 
505 8 |a 2.4 Time Encoding and Decoding in Shift-Invariant Spaces2.5 Conclusions; References; 3 A Novel Framework for Reconstructing Bandlimited Signals Encoded by Integrate-and-Fire Neurons; 3.1 Introduction; 3.2 A New Method of Reconstructing Functions from Local Averages; 3.3 Direct Reconstruction Algorithms for Inputs Encoded with Ideal IF Neurons; 3.4 The Integrate-and-Fire Neuron as a Uniform Sampler; 3.5 Fast Indirect Reconstruction Algorithms for Inputs Encoded with Ideal IF Neurons; 3.6 Numerical Study; 3.6.1 Numerical Study for Algorithm 3.1; 3.6.2 Numerical Study for Algorithm 3.2. 
505 8 |a 3.6.3 Error Evaluation for the Interpolation Step of the Proposed Algorithms3.7 Conclusions; References; 4 A Novel Reconstruction Framework in Shift-Invariant Spaces for Signals Encoded with Integrate-and-Fire Neurons; 4.1 Introduction; 4.2 A New Non-iterative Method for Reconstructing Signals in Shift-Invariant Spaces from Spike Trains Generated with IF-TEMs; 4.3 Direct Reconstruction Algorithms for Inputs Encoded with Ideal IF Neurons; 4.4 Fast Indirect Reconstruction Algorithms for Inputs Encoded with Ideal IF Neurons; 4.5 Numerical Study. 
505 8 |a 4.5.1 Comparative Numerical Study of the Iterative Algorithms4.5.2 Comparative Numerical Study of the Non-iterative Algorithms; 4.6 Conclusions; References; 5 A New Approach to the Identification of Sensory Processing Circuits Based on Spiking Neuron Data; 5.1 Introduction; 5.2 Identification of Spiking Neural Circuits; 5.2.1 Identification of [Linear Filter]-[Ideal IF] Circuits; 5.2.2 Identification Methods for Different Circuit Structures; 5.3 The NARMAX Identification Methodology; 5.3.1 An Overview of the NARMAX Model; 5.3.2 The Orthogonal Least Squares Estimator. 
505 8 |a 5.3.3 The Orthogonal Forward Regression Algorithm5.3.4 The Generalised Frequency Response Functions; 5.4 A New Method for the Identification of [Nonlinear Filter]-[Ideal IF] Circuits; 5.4.1 Problem Statement; 5.4.2 Numerical Study; 5.5 A New Methodology for the Identification of [Linear Filter]-[Leaky IF] Circuits; 5.5.1 Problem Statement; 5.5.2 Numerical Study; 5.6 Conclusions; References; 6 A New Method for Implementing Linear Filters in the Spike Domain; 6.1 Introduction; 6.2 Problem Statement; 6.3 Direct Computation of Spike Times; 6.4 Numerical Study; 6.5 Conclusions; References. 
504 |a Includes bibliographical references. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a 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 a linear filter, given the input of the filter encoded with the same neuron model. 
590 |a SpringerLink  |b Springer Complete eBooks 
650 0 |a Neural circuitry. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
776 0 8 |i Print version:  |a Florescu, Dorian.  |t Reconstruction, identification and implementation methods for spiking neural circuits.  |d Cham, Switzerland : Springer, 2017  |z 9783319570808  |z 3319570803  |w (OCoLC)978290096 
830 0 |a Springer theses. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://link.springer.com/10.1007/978-3-319-57081-5  |y Plný text 
992 |c NTK-SpringerENG 
999 |c 100046  |d 100046 
993 |x NEPOSILAT  |y EIZ 

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