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

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Titel: Information Transmitted From Bioinspired Neuron–Astrocyte Network Improves Cortical Spiking Network’s Pattern Recognition Performance.
Autoren: Nazari, Soheila, Amiri, Masoud, Faez, Karim, Van Hulle, Marc M.
Quelle: IEEE Transactions on Neural Networks & Learning Systems; Feb2020, Vol. 31 Issue 2, p464-474, 11p
Schlagwörter: SPHEROIDAL functions, WAVE functions, INTERNEURONS, PATTERN recognition systems
Abstract: 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]
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Datenbank: Biomedical Index
Beschreibung
Abstract: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]
ISSN:2162237X
DOI:10.1109/TNNLS.2019.2905003