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|a 9783319570815
|9 978-3-319-57081-5
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|a 10.1007/978-3-319-57081-5
|2 doi
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|a 621.382
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|a Florescu, Dorian.
|e author.
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|a Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits
|h [electronic resource] /
|c by Dorian Florescu.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2017.
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|a XIV, 139 p. 42 illus., 27 illus. in color.
|b online resource.
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|a text
|b txt
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|a computer
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|b PDF
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|a Springer Theses, Recognizing Outstanding Ph.D. Research,
|x 2190-5053
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|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.
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|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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|a Engineering.
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|a Neurosciences.
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|a System theory.
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|a Neural networks (Computer science).
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|a Electronic circuits.
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|a Engineering.
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|a Signal, Image and Speech Processing.
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|a Mathematical Models of Cognitive Processes and Neural Networks.
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|a Neurosciences.
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|a Systems Theory, Control.
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|a Circuits and Systems.
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783319570808
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|a Springer Theses, Recognizing Outstanding Ph.D. Research,
|x 2190-5053
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|u http://dx.doi.org/10.1007/978-3-319-57081-5
|z Full Text via HEAL-Link
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|a ZDB-2-ENG
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|a Engineering (Springer-11647)
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