SPAYK: an environment for spiking neural network simulation

ArticleAçık Erişim

Özet

In research areas such as mobile robotics and computer vision, energy and computational efficiency have become critical. This has greatly increased interest in high-efficiency neuromorphic hardware and spiking neural networks. Because neuromorphic hardware is not yet widely available, spiking neural network studies are conducted by simulations. There are numerous simulators available today, each designed for a specific purpose. In this paper, a novel and opensource package (SPAYK) for simulating spiking neural networks is presented. SPAYK has been proposed to speed up spiking neural network research. In the majority of simulators, networks are expressed with differential equations and require advanced neuroscience knowledge since such simulators are generally designed for brain and neuroscience research. SPAYK, on the other hand, is specifically designed as a framework to easily design spiking neural networks for practical problems. SPAYK is an easy-to-use Python package. There are three fundamental classes in the core: the model class for creating neuron groups, the organization class for simulating tissues, and the learning class for synaptic plasticity. While developing and testing the SPAYK environment, various experiments were carried out. This study includes three of these experiments. In the first experiment, we investigated the behavior of a group of Izhikevich neurons for visual stimuli. Also, a single Izhikevich neuron has been trained to respond to a particular label in a supervised manner with synaptic plasticity. In the second experiment, a well-known experiment was repeated to validate SPAYK. In this experiment, a neuron trained by synaptic plasticity can recognize repetitive patterns in a spike train. In the third experiment, a similar neuron was simulated with stimuli with multiple labels adapted from the MNIST dataset. It has been shown that the neuron can classify a particular label by synaptic plasticity. All these experiments and the SPAYK environment are presented as open-source tools. © 2023 TÜBİTAK.

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RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/ebyu/12297Resmi atıflarda kullanılan kalıcı adres
DOI
https://doi.org/10.55730/1300-0632.3995Yayıncı resmi DOI adresi
Tam Kayıt
AlanDeğerDil
dc.contributor.authorGelen, Aykut Görkem
dc.contributor.authorAtasoy, Ayten
dc.date.accessioned2026-10-18T20:27:12.677982625Z
dc.date.available2026-10-18T20:27:12.677982625Z
dc.date.issued2023
dc.description.abstractIn research areas such as mobile robotics and computer vision, energy and computational efficiency have become critical. This has greatly increased interest in high-efficiency neuromorphic hardware and spiking neural networks. Because neuromorphic hardware is not yet widely available, spiking neural network studies are conducted by simulations. There are numerous simulators available today, each designed for a specific purpose. In this paper, a novel and opensource package (SPAYK) for simulating spiking neural networks is presented. SPAYK has been proposed to speed up spiking neural network research. In the majority of simulators, networks are expressed with differential equations and require advanced neuroscience knowledge since such simulators are generally designed for brain and neuroscience research. SPAYK, on the other hand, is specifically designed as a framework to easily design spiking neural networks for practical problems. SPAYK is an easy-to-use Python package. There are three fundamental classes in the core: the model class for creating neuron groups, the organization class for simulating tissues, and the learning class for synaptic plasticity. While developing and testing the SPAYK environment, various experiments were carried out. This study includes three of these experiments. In the first experiment, we investigated the behavior of a group of Izhikevich neurons for visual stimuli. Also, a single Izhikevich neuron has been trained to respond to a particular label in a supervised manner with synaptic plasticity. In the second experiment, a well-known experiment was repeated to validate SPAYK. In this experiment, a neuron trained by synaptic plasticity can recognize repetitive patterns in a spike train. In the third experiment, a similar neuron was simulated with stimuli with multiple labels adapted from the MNIST dataset. It has been shown that the neuron can classify a particular label by synaptic plasticity. All these experiments and the SPAYK environment are presented as open-source tools. © 2023 TÜBİTAK.
dc.identifier.doi10.55730/1300-0632.3995
dc.identifier.eissn1303-6203
dc.identifier.endpage480
dc.identifier.issn1300-0632
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85151543035
dc.identifier.startpage462
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/ebyu/12297
dc.identifier.volume31
dc.identifier.wosWOS:000957927500014
dc.language.isoen
dc.publisherTurkiye Klinikleri
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectSpiking neural network
dc.subjectSTDP based learning
dc.subjectsupervised classification
dc.subjectunsupervised pattern recognition
dc.subjectComputational efficiency
dc.subjectComputer hardware
dc.subjectDifferential equations
dc.subjectNeurons
dc.subjectPattern recognition
dc.subjectIzhikevich neurons
dc.subjectNeural-networks
dc.subjectNeuromorphic hardwares
dc.subjectSpiking neural network simulations
dc.subjectSynaptic plasticity
dc.subjectUnsupervised patterns
dc.subjectNeural networks
dc.titleSPAYK: an environment for spiking neural network simulation
dc.typeArticle
dspace.entity.typePublication
rdl.citation.scopus2
rdl.citation.trdizin0
rdl.citation.wos2
rdl.departmentERZİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİ
rdl.departmentKARADENİZ TEKNİK ÜNİVERSİTESİ
rdl.oa.statusgold
rdl.source.indexSCOPUS
rdl.source.indexWOS
rdl.source.indexTRDIZIN
rdl.wos.indexScience Citation Index Expanded (SCI-EXPANDED)