Hardcover. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits (Hardcover)
Editor: Springer International Publishing AG, Cham
Fecha de publicaci�n: 2017
Idioma: Ingl�s
ISBN 10: 3319570803
ISBN 13: 9783319570808
Encuadernaci�n: Hardcover
Edici�n: 1� Edici�n
Estado: new

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Sinopsis

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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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

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