Error-Based Analysis of Optimal Tuning Functions Explains Phenomena Observed in Sensory Neurons
Identifieur interne : 003739 ( Main/Exploration ); précédent : 003738; suivant : 003740Error-Based Analysis of Optimal Tuning Functions Explains Phenomena Observed in Sensory Neurons
Auteurs : Steve Yaeli [Israël] ; Ron Meir [Israël]Source :
- Frontiers in Computational Neuroscience [ 1662-5188 ] ; 2010.
Abstract
Biological systems display impressive capabilities in effectively responding to environmental signals in real time. There is increasing evidence that organisms may indeed be employing near optimal Bayesian calculations in their decision-making. An intriguing question relates to the properties of optimal encoding methods, namely determining the properties of neural populations in sensory layers that optimize performance, subject to physiological constraints. Within an ecological theory of neural encoding/decoding, we show that optimal Bayesian performance requires neural adaptation which reflects environmental changes. Specifically, we predict that neuronal tuning functions possess an optimal width, which increases with prior uncertainty and environmental noise, and decreases with the decoding time window. Furthermore, even for static stimuli, we demonstrate that dynamic sensory tuning functions, acting at relatively short time scales, lead to improved performance. Interestingly, the narrowing of tuning functions as a function of time was recently observed in several biological systems. Such results set the stage for a functional theory which may explain the high reliability of sensory systems, and the utility of neuronal adaptation occurring at multiple time scales.
Url:
DOI: 10.3389/fncom.2010.00130
PubMed: 21079749
PubMed Central: 2978036
Affiliations:
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There is increasing evidence that organisms may indeed be employing near optimal Bayesian calculations in their decision-making. An intriguing question relates to the properties of optimal encoding methods, namely determining the properties of neural populations in sensory layers that optimize performance, subject to physiological constraints. Within an ecological theory of neural encoding/decoding, we show that optimal Bayesian performance requires neural adaptation which reflects environmental changes. Specifically, we predict that neuronal tuning functions possess an optimal width, which increases with prior uncertainty and environmental noise, and decreases with the decoding time window. Furthermore, even for static stimuli, we demonstrate that dynamic sensory tuning functions, acting at relatively short time scales, lead to improved performance. Interestingly, the narrowing of tuning functions as a function of time was recently observed in several biological systems. Such results set the stage for a functional theory which may explain the high reliability of sensory systems, and the utility of neuronal adaptation occurring at multiple time scales.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Anderson, J" uniqKey="Anderson J">J. Anderson</name></author><author><name sortKey="Lampl, I" uniqKey="Lampl I">I. Lampl</name></author><author><name sortKey="Gillespie, D" uniqKey="Gillespie D">D. Gillespie</name></author><author><name sortKey="Ferster, D" uniqKey="Ferster D">D. Ferster</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Averbeck, B" uniqKey="Averbeck B">B. Averbeck</name></author><author><name sortKey="Latham, P" uniqKey="Latham P">P. Latham</name></author><author><name sortKey="Pouget, A" uniqKey="Pouget A">A. Pouget</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Baccus, S" uniqKey="Baccus S">S. 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Sejnowski</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Israël</li></country></list><tree><country name="Israël"><noRegion><name sortKey="Yaeli, Steve" sort="Yaeli, Steve" uniqKey="Yaeli S" first="Steve" last="Yaeli">Steve Yaeli</name></noRegion><name sortKey="Meir, Ron" sort="Meir, Ron" uniqKey="Meir R" first="Ron" last="Meir">Ron Meir</name><name sortKey="Meir, Ron" sort="Meir, Ron" uniqKey="Meir R" first="Ron" last="Meir">Ron Meir</name><name sortKey="Yaeli, Steve" sort="Yaeli, Steve" uniqKey="Yaeli S" first="Steve" last="Yaeli">Steve Yaeli</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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