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Information Transmitted From Bioinspired Neuron–Astrocyte Network Improves Cortical Spiking Network’s Pattern Recognition Performance.

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Názov: Information Transmitted From Bioinspired Neuron–Astrocyte Network Improves Cortical Spiking Network’s Pattern Recognition Performance.
Autori: Nazari, Soheila, Amiri, Masoud, Faez, Karim, Van Hulle, Marc M.
Zdroj: IEEE Transactions on Neural Networks & Learning Systems; Feb2020, Vol. 31 Issue 2, p464-474, 11p
Predmety: SPHEROIDAL functions, WAVE functions, INTERNEURONS, PATTERN recognition systems
Abstrakt: We trained two spiking neural networks (SNNs), the cortical spiking network (CSN) and the cortical neuron–astrocyte network (CNAN), using a spike-based unsupervised method, on the MNIST and alpha-digit data sets and achieve an accuracy of 96.1% and 77.35%, respectively. We then connected CNAN to CSN by preserving maximum synchronization between them thanks to the concept of prolate spheroidal wave functions (PSWF). As a result, CSN receives additional information from CNAN without retraining. The important outcome is that CSN reaches 70.57% correct classification rate on capital letters without being trained on them. The overall contribution of transfer is 87.47%. We observed that for CSN the classifying neurons that relate to digits 0–9 of the alpha-digit data set are completely supported by the ones that relate to digits 0–9 of the MNIST data set. This means that CSN recognizes the similarity between the digits of the MNIST and alpha-digit data sets and classifies each digit of both data sets in the same class. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Neural Networks & Learning Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáza: Biomedical Index
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  Data: Information Transmitted From Bioinspired Neuron–Astrocyte Network Improves Cortical Spiking Network’s Pattern Recognition Performance.
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  Data: <searchLink fieldCode="AR" term="%22Nazari%2C+Soheila%22">Nazari, Soheila</searchLink><br /><searchLink fieldCode="AR" term="%22Amiri%2C+Masoud%22">Amiri, Masoud</searchLink><br /><searchLink fieldCode="AR" term="%22Faez%2C+Karim%22">Faez, Karim</searchLink><br /><searchLink fieldCode="AR" term="%22Van+Hulle%2C+Marc+M%2E%22">Van Hulle, Marc M.</searchLink>
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  Data: IEEE Transactions on Neural Networks & Learning Systems; Feb2020, Vol. 31 Issue 2, p464-474, 11p
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– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: We trained two spiking neural networks (SNNs), the cortical spiking network (CSN) and the cortical neuron–astrocyte network (CNAN), using a spike-based unsupervised method, on the MNIST and alpha-digit data sets and achieve an accuracy of 96.1% and 77.35%, respectively. We then connected CNAN to CSN by preserving maximum synchronization between them thanks to the concept of prolate spheroidal wave functions (PSWF). As a result, CSN receives additional information from CNAN without retraining. The important outcome is that CSN reaches 70.57% correct classification rate on capital letters without being trained on them. The overall contribution of transfer is 87.47%. We observed that for CSN the classifying neurons that relate to digits 0–9 of the alpha-digit data set are completely supported by the ones that relate to digits 0–9 of the MNIST data set. This means that CSN recognizes the similarity between the digits of the MNIST and alpha-digit data sets and classifies each digit of both data sets in the same class. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of IEEE Transactions on Neural Networks & Learning Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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      – SubjectFull: WAVE functions
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      – TitleFull: Information Transmitted From Bioinspired Neuron–Astrocyte Network Improves Cortical Spiking Network’s Pattern Recognition Performance.
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              Text: Feb2020
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