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020 _a9783319570815
_9978-3-319-57081-5
024 7 _a10.1007/978-3-319-57081-5
_2doi
050 4 _aTK5102.9
072 7 _aTJF
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aTJF
_2thema
072 7 _aUYS
_2thema
082 0 4 _a621.382
_223
100 1 _aFlorescu, Dorian.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_962913
245 1 0 _aReconstruction, Identification and Implementation Methods for Spiking Neural Circuits
_h[electronic resource] /
_cby Dorian Florescu.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXIV, 139 p. 42 illus., 27 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5061
505 0 _aNomenclature -- 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 _aThis 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.
650 0 _aSignal processing.
_94052
650 0 _aNeural networks (Computer science) .
_962914
650 0 _aNeurosciences.
_924499
650 0 _aSystem theory.
_93409
650 0 _aControl theory.
_93950
650 0 _aElectronic circuits.
_919581
650 1 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
650 2 4 _aNeuroscience.
_934310
650 2 4 _aSystems Theory, Control .
_931597
650 2 4 _aElectronic Circuits and Systems.
_962915
710 2 _aSpringerLink (Online service)
_962916
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319570808
776 0 8 _iPrinted edition:
_z9783319570822
776 0 8 _iPrinted edition:
_z9783319860725
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5061
_962917
856 4 0 _uhttps://doi.org/10.1007/978-3-319-57081-5
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c81068
_d81068