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Reconstruction, identification and i...

Florescu, Dorian.

Reconstruction, identification and implementation methods for spiking neural circuits

Record Type:	Electronic resources : Monograph/item
Title/Author:	Reconstruction, identification and implementation methods for spiking neural circuitsby Dorian Florescu.
Author:	Florescu, Dorian.
Published:	Cham :Springer International Publishing :2017.
Description:	xiv, 139 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Neural circuitry.
Online resource:	http://dx.doi.org/10.1007/978-3-319-57081-5
ISBN:	9783319570815$q(electronic bk.)

Reconstruction, identification and implementation methods for spiking neural circuits
Florescu, Dorian.

Reconstruction, identification and implementation methods for spiking neural circuits[electronic resource] /by Dorian Florescu. - Cham :Springer International Publishing :2017. - xiv, 139 p. :ill. (some col.), digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

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.

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.

ISBN: 9783319570815$q(electronic bk.)

Standard No.: 10.1007/978-3-319-57081-5doiSubjects--Topical Terms:

301346
Neural circuitry.

LC Class. No.: QP363.3

Dewey Class. No.: 573.85

Reconstruction, identification and implementation methods for spiking neural circuits
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505 0 $a 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 $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.
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