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Statements

Subject Item
n2:RIV%2F00216224%3A14310%2F07%3A00022737%21RIV10-MSM-14310___
rdf:type
skos:Concept n7:Vysledek
dcterms:description
To study sensory neurons, the neuron response is plotted versus stimulus level. The aim of the present contribution is to determine how well two different levels of the incoming stimulation can be distinguished on the basis of their evoked responses. Two generic models of response function are presented and studied under the influence of noise. We show that the most suitable signal, from the point of view of its identification, is not unique. To obtain the best identification we propose to use measures based on Fisher information. For these measures, we show that the most identifiable signal may differ from that derived when the noise is neglected. To study sensory neurons, the neuron response is plotted versus stimulus level. The aim of the present contribution is to determine how well two different levels of the incoming stimulation can be distinguished on the basis of their evoked responses. Two generic models of response function are presented and studied under the influence of noise. We show that the most suitable signal, from the point of view of its identification, is not unique. To obtain the best identification we propose to use measures based on Fisher information. For these measures, we show that the most identifiable signal may differ from that derived when the noise is neglected.
dcterms:title
Stimulus-Response Curves in Sensory Neurons: How to Find the Stimulus Measurable with the Highest Precision Stimulus-Response Curves in Sensory Neurons: How to Find the Stimulus Measurable with the Highest Precision
skos:prefLabel
Stimulus-Response Curves in Sensory Neurons: How to Find the Stimulus Measurable with the Highest Precision Stimulus-Response Curves in Sensory Neurons: How to Find the Stimulus Measurable with the Highest Precision
skos:notation
RIV/00216224:14310/07:00022737!RIV10-MSM-14310___
n3:aktivita
n11:Z n11:P
n3:aktivity
P(1ET400110401), P(GD201/05/H007), P(LC06024), P(LC554), Z(AV0Z50110509)
n3:dodaniDat
n4:2010
n3:domaciTvurceVysledku
n22:2989387 n22:1350374
n3:druhVysledku
n16:D
n3:duvernostUdaju
n8:S
n3:entitaPredkladatele
n9:predkladatel
n3:idSjednocenehoVysledku
452529
n3:idVysledku
RIV/00216224:14310/07:00022737
n3:jazykVysledku
n17:eng
n3:klicovaSlova
neuronal coding
n3:klicoveSlovo
n21:neuronal%20coding
n3:kontrolniKodProRIV
[B1E6EA613004]
n3:mistoKonaniAkce
Naples
n3:mistoVydani
Berlin / Heidelberg
n3:nazevZdroje
Advances in Brain, Vision, and Artificial Intelligence, Lecture Notes in Computer Science 4729
n3:obor
n20:BB
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:projekt
n10:1ET400110401 n10:LC06024 n10:LC554 n10:GD201%2F05%2FH007
n3:rokUplatneniVysledku
n4:2007
n3:tvurceVysledku
Rospars, Jean-Pierre Lánský, Petr Pokora, Ondřej
n3:typAkce
n13:WRD
n3:wos
000250716000032
n3:zahajeniAkce
2007-10-10+02:00
n3:zamer
n18:AV0Z50110509
s:numberOfPages
12
n19:hasPublisher
Springer-Verlag
n15:isbn
978-3-540-75554-8
n12:organizacniJednotka
14310