CRBA: A Competitive Rate-Based Algorithm Based on Competitive Spiking Neural Networks

In this paper we present a Competitive Rate-Based Algorithm (CRBA) that approximates operation of a Competitive Spiking Neural Network (CSNN). CRBA is based on modeling of the competition between neurons during a sample presentation, which can be reduced to ranking of the neurons based on a dot prod...

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Published in:	Frontiers in computational neuroscience Vol. 15; p. 627567
Main Authors:	Cachi, Paolo G., Ventura, Sebastián, Cios, Krzysztof J.
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Research Foundation 22.04.2021 Frontiers Media S.A
Subjects:	Algorithms Back propagation Competition competitive learning competitive spiking neural networks Computers Fashion models Firing pattern Learning MNIST Neural networks Neurons Neuroscience rate-based algorithm unsupervised image classification MNIST competitive learning competitive spiking neural networks rate-based algorithm unsupervised image classification
ISSN:	1662-5188, 1662-5188
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Abstract	In this paper we present a Competitive Rate-Based Algorithm (CRBA) that approximates operation of a Competitive Spiking Neural Network (CSNN). CRBA is based on modeling of the competition between neurons during a sample presentation, which can be reduced to ranking of the neurons based on a dot product operation and the use of a discrete Expectation Maximization algorithm; the latter is equivalent to the spike time-dependent plasticity rule. CRBA's performance is compared with that of CSNN on the MNIST and Fashion-MNIST datasets. The results show that CRBA performs on par with CSNN, while using three orders of magnitude less computational time. Importantly, we show that the weights and firing thresholds learned by CRBA can be used to initialize CSNN's parameters that results in its much more efficient operation.
AbstractList	In this paper we present a Competitive Rate-Based Algorithm (CRBA) that approximates operation of a Competitive Spiking Neural Network (CSNN). CRBA is based on modeling of the competition between neurons during a sample presentation, which can be reduced to ranking of the neurons based on a dot product operation and the use of a discrete Expectation Maximization algorithm; the latter is equivalent to the spike time-dependent plasticity rule. CRBA's performance is compared with that of CSNN on the MNIST and Fashion-MNIST datasets. The results show that CRBA performs on par with CSNN, while using three orders of magnitude less computational time. Importantly, we show that the weights and firing thresholds learned by CRBA can be used to initialize CSNN's parameters that results in its much more efficient operation.In this paper we present a Competitive Rate-Based Algorithm (CRBA) that approximates operation of a Competitive Spiking Neural Network (CSNN). CRBA is based on modeling of the competition between neurons during a sample presentation, which can be reduced to ranking of the neurons based on a dot product operation and the use of a discrete Expectation Maximization algorithm; the latter is equivalent to the spike time-dependent plasticity rule. CRBA's performance is compared with that of CSNN on the MNIST and Fashion-MNIST datasets. The results show that CRBA performs on par with CSNN, while using three orders of magnitude less computational time. Importantly, we show that the weights and firing thresholds learned by CRBA can be used to initialize CSNN's parameters that results in its much more efficient operation. In this paper we present a Competitive Rate-Based Algorithm (CRBA) that approximates operation of a Competitive Spiking Neural Network (CSNN). CRBA is based on modeling of the competition between neurons during a sample presentation, which can be reduced to ranking of the neurons based on a dot product operation and the use of a discrete Expectation Maximization algorithm; the latter is equivalent to the spike time-dependent plasticity rule. CRBA’s performance is compared with that of CSNN on the MNIST and Fashion-MNIST datasets. The results show that CRBA performs on par with CSNN, while using three orders of magnitude less computational time. Importantly, we show that the weights and firing thresholds learned by CRBA can be used to initialize CSNN's parameters that results in its much more efficient operation.
Author	Cachi, Paolo G. Cios, Krzysztof J. Ventura, Sebastián
AuthorAffiliation	3 Polish Academy of Sciences , Gliwice , Poland 1 Department of Computer Science, Virginia Commonwealth University , Richmond, VA , United States 2 Department of Computer Science, Universidad de Córdoba , Córdoba , Spain
AuthorAffiliation_xml	– name: 3 Polish Academy of Sciences , Gliwice , Poland – name: 2 Department of Computer Science, Universidad de Córdoba , Córdoba , Spain – name: 1 Department of Computer Science, Virginia Commonwealth University , Richmond, VA , United States
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/33967726$$D View this record in MEDLINE/PubMed
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Keywords	MNIST competitive learning competitive spiking neural networks rate-based algorithm unsupervised image classification
Language	English
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SubjectTerms	Algorithms Back propagation Competition competitive learning competitive spiking neural networks Computers Fashion models Firing pattern Learning MNIST Neural networks Neurons Neuroscience rate-based algorithm unsupervised image classification
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Title	CRBA: A Competitive Rate-Based Algorithm Based on Competitive Spiking Neural Networks
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