Detailed Information on Publication Record
2008
Classification of stimuli based on stimulus-response curves and their variability
LÁNSKÝ, Petr, Ondřej POKORA and Jean-Pierre ROSPARSBasic information
Original name
Classification of stimuli based on stimulus-response curves and their variability
Name in Czech
Klasifikace stimulů podle křivek stimulus-odpověď a jejich variabilitě
Authors
LÁNSKÝ, Petr (203 Czech Republic, belonging to the institution), Ondřej POKORA (203 Czech Republic, guarantor, belonging to the institution) and Jean-Pierre ROSPARS (250 France)
Edition
Brain Research, Amsterdam, Elsevier, 2008, 0006-8993
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10000 1. Natural Sciences
Country of publisher
Netherlands
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 2.494
RIV identification code
RIV/00216224:14310/08:00024208
Organization unit
Faculty of Science
UT WoS
000258954600007
Keywords in English
Fisher information; Information theory; Noise; Response curve; Sensory neurons; Stimulus identification
Tags
Tags
International impact, Reviewed
Změněno: 13/1/2015 23:09, Mgr. Ondřej Pokora, Ph.D.
V originále
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.
In Czech
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.
Links
| GD201/05/H007, research and development project |
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| LC06024, research and development project |
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