LÁNSKÝ, Petr, Ondřej POKORA a Jean-Pierre ROSPARS. Classification of stimuli based on stimulus-response curves and their variability. Brain Research. Amsterdam: Elsevier, Volume 122, srpen 2008, s. 57-66. ISSN 0006-8993. 2008.
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Základní údaje
Originální název Classification of stimuli based on stimulus-response curves and their variability
Název česky Klasifikace stimulů podle křivek stimulus-odpověď a jejich variabilitě
Autoři LÁNSKÝ, Petr (203 Česká republika, domácí), Ondřej POKORA (203 Česká republika, garant, domácí) a Jean-Pierre ROSPARS (250 Francie).
Vydání Brain Research, Amsterdam, Elsevier, 2008, 0006-8993.
Další údaje
Originální jazyk angličtina
Typ výsledku Článek v odborném periodiku
Obor 10000 1. Natural Sciences
Stát vydavatele Nizozemské království
Utajení není předmětem státního či obchodního tajemství
WWW doi:10.1016/j.brainres.2008.04.058
Impakt faktor Impact factor: 2.494
Kód RIV RIV/00216224:14310/08:00024208
Organizační jednotka Přírodovědecká fakulta
UT WoS 000258954600007
Klíčová slova anglicky Fisher information; Information theory; Noise; Response curve; Sensory neurons; Stimulus identification
Štítky Fisher information, information theory, noise, Response curve, Sensory neurons, Stimulus identification
Příznaky Mezinárodní význam, Recenzováno
Změnil Změnil: Mgr. Ondřej Pokora, Ph.D., učo 42536. Změněno: 13. 1. 2015 23:09.
Anotace
Neuronal responses evoked in sensory neurons by static stimuli of various intensities are usually characterized by their input-output transfer function, i.e. by plotting the firing frequency (or any other measurable neuron response) versus the corresponding stimulus intensity. The aim of the present article is to determine the stimulus intensities which can be considered as "the most important" from two different points of view: transferring as much information as possible and coding the intensity as precisely as possible. These two problems are very different because, for example, an informative signal may be difficult to identify. We show that the role of noise is crucial in both problems. To obtain the range of stimuli which are the best identified, we propose to use measures based on Fisher information as known from the theory of statistical inference. To classify the most important stimuli from the point of view of information transfer, we suggest methods based on information theory. We show that both the most identifiable signal and the most informative signal are not unique. To study this, a generic model of input-output transfer function is analyzed under the influence of several different types of noise. Finally, the methods are illustrated on a model and data pertaining to olfactory sensory neurons.
Anotace česky
Neuronal responses evoked in sensory neurons by static stimuli of various intensities are usually characterized by their input-output transfer function, i.e. by plotting the firing frequency (or any other measurable neuron response) versus the corresponding stimulus intensity. The aim of the present article is to determine the stimulus intensities which can be considered as "the most important" from two different points of view: transferring as much information as possible and coding the intensity as precisely as possible. These two problems are very different because, for example, an informative signal may be difficult to identify. We show that the role of noise is crucial in both problems. To obtain the range of stimuli which are the best identified, we propose to use measures based on Fisher information as known from the theory of statistical inference. To classify the most important stimuli from the point of view of information transfer, we suggest methods based on information theory. We show that both the most identifiable signal and the most informative signal are not unique. To study this, a generic model of input-output transfer function is analyzed under the influence of several different types of noise. Finally, the methods are illustrated on a model and data pertaining to olfactory sensory neurons.
Návaznosti
GD201/05/H007, projekt VaVNázev: Statistické dynamické modely a jejich aplikace v ekonomických, přírodovědných a technických oborech
Investor: Grantová agentura ČR, Statistické dynamické modely a jejich aplikace v ekonomických, přírodovědných a technických oborech
LC06024, projekt VaVNázev: Centrum Jaroslava Hájka pro teoretickou a aplikovanou statistiku
Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Centrum Jaroslava Hájka pro teoretickou a aplikovanou statistiku
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