Publication date
August 2015
DOI Publisher
Springer Science and Business Media LLC Language
EnglishAbstract
http://deepblue.lib.umich.edu/bitstream/2027.42/112572/1/12868_2012_Article_2540.pd
http://deepblue.lib.umich.edu/bitstream/2027.42/112572/1/12868_2012_Article_2540.pd
Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing ...
SummaryIn several sensory pathways, input stimuli project to sparsely active downstream populations ...
Considering that many natural stimuli are sparse, can a sensory system evolve to take advantage of t...
proposed to capture the information present in sensory stimuli and to encode it into spike trains [1...
"Sparse" neural networks, in which relatively few neurons or connections are active, are common in b...
Sparse coding schemes are employed by many sensory systems and implement efficient coding principles...
The theory of Compressive Sensing (CS) exploits a well-known concept used in signal compression – sp...
International audienceSparse signal decomposition are keys to efficient compression, storage and den...
© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Att...
Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven hig...
Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven hig...
A combination of experimental and theoretical studies have postulated converging evidence for the hy...
The activations of an analog neural network (ANN) are usually treated as representing an analog firi...
Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven hig...
International audienceIf modern computers are sometimes superior to humans in some specialized tasks...
Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing ...
SummaryIn several sensory pathways, input stimuli project to sparsely active downstream populations ...
Considering that many natural stimuli are sparse, can a sensory system evolve to take advantage of t...
proposed to capture the information present in sensory stimuli and to encode it into spike trains [1...
"Sparse" neural networks, in which relatively few neurons or connections are active, are common in b...
Sparse coding schemes are employed by many sensory systems and implement efficient coding principles...
The theory of Compressive Sensing (CS) exploits a well-known concept used in signal compression – sp...
International audienceSparse signal decomposition are keys to efficient compression, storage and den...
© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Att...
Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven hig...
Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven hig...
A combination of experimental and theoretical studies have postulated converging evidence for the hy...
The activations of an analog neural network (ANN) are usually treated as representing an analog firi...
Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven hig...
International audienceIf modern computers are sometimes superior to humans in some specialized tasks...
Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing ...
SummaryIn several sensory pathways, input stimuli project to sparsely active downstream populations ...
Considering that many natural stimuli are sparse, can a sensory system evolve to take advantage of t...
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