Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits (Record no. 81068)

000 -LEADER
fixed length control field 03950nam a22006255i 4500
001 - CONTROL NUMBER
control field 978-3-319-57081-5
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220801222715.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 170424s2017 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319570815
-- 978-3-319-57081-5
082 04 - CLASSIFICATION NUMBER
Call Number 621.382
100 1# - AUTHOR NAME
Author Florescu, Dorian.
245 10 - TITLE STATEMENT
Title Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2017.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XIV, 139 p. 42 illus., 27 illus. in color.
490 1# - SERIES STATEMENT
Series statement Springer Theses, Recognizing Outstanding Ph.D. Research,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Nomenclature -- Acronyms -- 1 Introduction -- 2 Time Encoding and Decoding in Bandlimited and Shift-Invariant Spaces -- 3 A Novel Framework for Reconstructing Bandlimited Signals Encoded by Integrate and-Fire Neurons -- 4 A Novel Reconstruction Framework in Shift-Invariant Spaces for Signals Encoded with Integrate-and-Fire Neurons -- 5 A New Approach to the Identification of Sensory Processing Circuits Based on Spiking Neuron Data -- 6 A New Method for Implementing Linear Filters in the Spike Domain -- 7 Conclusions and Future Work -- Bibliography.
520 ## - SUMMARY, ETC.
Summary, etc 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.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-319-57081-5
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2017.
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-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
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-- online resource
-- cr
-- rdacarrier
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-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Signal processing.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Neural networks (Computer science) .
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Neurosciences.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- System theory.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Control theory.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Electronic circuits.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Signal, Speech and Image Processing .
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Mathematical Models of Cognitive Processes and Neural Networks.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Neuroscience.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Systems Theory, Control .
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Electronic Circuits and Systems.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2190-5061
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-- ZDB-2-ENG
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-- ZDB-2-SXE

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