Stochastic pooling networks

Stochastic pooling networks

Source :

• Journal of Statistical Mechanics: Theory and Experiment [ 1742-5468 ] ; 2009.

Descripteurs français

• Wicri :
  • topic : étude de cas.

English descriptors

• KwdEn :
  • Action potentials, Additive noise, Bernoulli distribution, Binary, Binary quantizing nodes, Biological neurons, Cambridge university press, Case studies, Case study, Cochlear, Cochlear implants, Cochlear nerve, Common signal, Communications networks, Converter circuit, Current paper, Digitized, Digitized beamforming, Digitized signals, Electrical stimulation, Emergent properties, Emergent property, Essential features, Exponential, Exponential family, External noise, Fewer states, Fluctuation noise lett, Further compression, Further work, Identical nodes, Ieee, Ieee trans, Independent variability, Individual measurements, Individual node outputs, Individual nodes, Information loss, Information source, Information theory, Input signal, Lett, Linear statistic, Lossless compression, Lossy, Lossy compression, Mcdonnell, Mech, Multiaccess channels, Multinomial trial, Multinomial trials, Multiple measurements, Mutual information, Negligible information, Negligible information loss, Negligible loss, Negligible reduction, Network nodes, Network output, Neural activity, Neuron, Node, Node outputs, Noise distribution, Noise reduction, Optimal fusion, Other hand, Output measurement, Overall network output, Phys, Physical channel, Physical properties, Poisson, Poisson case, Possible states, Quantizing, Quantizing nodes, Random noise, Random variables, Same sample, Same signal, Scenario, Sensor, Sensor networks, Signal process, Signal processing, Signal processing goal, Signal processing literature, Single measurement, Single node, Spns, Stat, Statistic, Statistical physics, Stochastic, Stochastic observations, Stochastic resonance, Such networks, Summation, Summation equation, Suprathreshold, Unsolved problems, Whole network.
• Teeft :
  • Action potentials, Additive noise, Bernoulli distribution, Binary, Binary quantizing nodes, Biological neurons, Cambridge university press, Case studies, Case study, Cochlear, Cochlear implants, Cochlear nerve, Common signal, Communications networks, Converter circuit, Current paper, Digitized, Digitized beamforming, Digitized signals, Electrical stimulation, Emergent properties, Emergent property, Essential features, Exponential, Exponential family, External noise, Fewer states, Fluctuation noise lett, Further compression, Further work, Identical nodes, Ieee, Ieee trans, Independent variability, Individual measurements, Individual node outputs, Individual nodes, Information loss, Information source, Information theory, Input signal, Lett, Linear statistic, Lossless compression, Lossy, Lossy compression, Mcdonnell, Mech, Multiaccess channels, Multinomial trial, Multinomial trials, Multiple measurements, Mutual information, Negligible information, Negligible information loss, Negligible loss, Negligible reduction, Network nodes, Network output, Neural activity, Neuron, Node, Node outputs, Noise distribution, Noise reduction, Optimal fusion, Other hand, Output measurement, Overall network output, Phys, Physical channel, Physical properties, Poisson, Poisson case, Possible states, Quantizing, Quantizing nodes, Random noise, Random variables, Same sample, Same signal, Scenario, Sensor, Sensor networks, Signal process, Signal processing, Signal processing goal, Signal processing literature, Single measurement, Single node, Spns, Stat, Statistic, Statistical physics, Stochastic, Stochastic observations, Stochastic resonance, Such networks, Summation, Summation equation, Suprathreshold, Unsolved problems, Whole network.

Abstract

• https://api.istex.fr/document/3266049B05A3CF60E032DB35B700B578291207DB/fulltext/pdf

Affiliations:

• Australie, France, Royaume-Uni

Links toward previous steps (curation, corpus...)

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