Handwritten Digit Recognition using Spiking Neural Networks

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dc.authorscopusid57796705800
dc.authorscopusid23980961100
dc.authorscopusid36195342900
dc.contributor.authorOzcan, O.
dc.contributor.authorOniz, Y.
dc.contributor.authorAyyildiz, M.
dc.date.accessioned2024-07-18T20:17:04Z
dc.date.available2024-07-18T20:17:04Z
dc.date.issued2022
dc.description4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2022 -- 9 June 2022 through 11 June 2022 -- -- 180434en_US
dc.description.abstractIn this work, both simulated and experimental studies have been carried out using Spiking Neural Networks (SNNs) on the handwritten digit recognition problem. The design of SNN is performed using Spike Response Model (SRM). A gradient based algorithm is applied for the learning of SNN. For the simulations, the proposed algorithms have been applied on the MNIST data set. To provide a basis for comparison, the same studies have been also performed for an equivalent non-spiking artificial neural network (ANN) structure. Following the simulated studies, experiments utilizing a multi touch IR frame have been carried out for both network structures. Two different approaches have been adopted to evaluate the performance of the proposed network models: In the first one, the training of the network structures has been accomplished on the MNIST data set, whereas the data acquired from the experimental setup have been used in the testing phase. Next, the data obtained from the experimental setup have been employed in both training and testing of the neural networks. The results of the simulations and experimental studies reveal that the SNN outperforms the conventional ANN. © 2022 IEEE.en_US
dc.identifier.doi10.1109/HORA55278.2022.9799818
dc.identifier.isbn9781665468350
dc.identifier.scopus2-s2.0-85133971493en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/HORA55278.2022.9799818
dc.identifier.urihttps://hdl.handle.net/11411/6398
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofHORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectHandwritten Digit Recognitionen_US
dc.subjectSpiking Neural Networksen_US
dc.subjectCharacter Recognitionen_US
dc.subjectStatistical Testsen_US
dc.subjectData Seten_US
dc.subjectGradient Based Algorithmen_US
dc.subjectHandwritten Digit Recognitionen_US
dc.subjectMulti-Touchen_US
dc.subjectNetwork Structuresen_US
dc.subjectNeural Networks Structureen_US
dc.subjectNeural-Networksen_US
dc.subjectPerformanceen_US
dc.subjectSpike Response Modelsen_US
dc.subjectSpiking Neural Networken_US
dc.subjectNeural Networksen_US
dc.titleHandwritten Digit Recognition using Spiking Neural Networksen_US
dc.typeConference Objecten_US

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