COI code: ECMECONF01_001
Paper Language: English
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Authors MNIST Recognition Using Unsupervised Biologically LearningSoheila Nazari - Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Karim faez - Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Abstract:In this paper, spiking neural networks (SNN) inspired by the model of local cortical population as a biologicalneuro-computing resource for digit recognition was presented. SNN was equipped with spike-based unsupervisedweight optimization based on the dynamical behavior of the excitatory (AMPA) and inhibitory (GABA) synapsesusing Spike Timing Dependent Plasticity (STDP). There are two main reasons why this structure is state of the artcompared to previous works: learning process is compatible with many experimental observations on induction oflong-term potentiation and long-term depression, image to signal mapping created an informative signal of theimage based on sequences of prolate spheroidal wave functions (PSWFs). Cortical SNN compared toearlier related studies recognized MNIST digits more accurate and achieved 96.1% classificationaccuracy with unsupervised learning based on sparse spike activity.
Keywords:Spiking Neural Network, STDP, MNIST Recognition, Biologically Learning
COI code: ECMECONF01_001
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Nazari, Soheila & Karim faez, 2018, MNIST Recognition Using Unsupervised Biologically Learning, The first national conference on applied research in electrical engineering, computer science and medical engineering, شيروان, موسسه پژوهشي رهجويان پايا شهر اترك, https://www.civilica.com/Paper-ECMECONF01-ECMECONF01_001.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Nazari, Soheila & Karim faez, 2018)
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Type: state university
Paper No.: 19748
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