About: Stimulus-Response Curves in Sensory Neurons: How to Find the Stimulus Measurable with the Highest Precision     Goto   Sponge   NotDistinct   Permalink

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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. (en)
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 (en)
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 (en)
skos:notation
  • RIV/00216224:14310/07:00022737!RIV10-MSM-14310___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1ET400110401), P(GD201/05/H007), P(LC06024), P(LC554), Z(AV0Z50110509)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 452529
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14310/07:00022737
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • neuronal coding (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [B1E6EA613004]
http://linked.open...v/mistoKonaniAkce
  • Naples
http://linked.open...i/riv/mistoVydani
  • Berlin / Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Advances in Brain, Vision, and Artificial Intelligence, Lecture Notes in Computer Science 4729
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Lánský, Petr
  • Pokora, Ondřej
  • Rospars, Jean-Pierre
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000250716000032
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 978-3-540-75554-8
http://localhost/t...ganizacniJednotka
  • 14310
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