{ "article_id": 872, "public_id": "9cebae58-6e5b-4675-baf3-b919e62b7ff1", "internal_identifier": null, "source_identifier": "39:237", "status": "published", "type": "research_article", "title": "Izhikevich Sinir Hücresi Modelinin Raspberry Pi Kullanılarak Gerçekleştirilmesi", "title_secondary": null, "abstract": "Canlılar koşma, yürüme ve emekleme gibi temel hareketlerini gerçekleştirmek için Merkezi Desen Üreteçleri olarak\r\nisimlendirilen ritmik desenler üreten sinir hücreleri topluluğuna sahiptir. Merkezi Desen Üreteçleri, canlıların bu temel\r\nhareketlerine ek olarak kalp atışı, solunum gibi ritmik olayları gerçekleştirmek için de görev alırlar. Merkezi Desen Üreteçleri’\r\nnin en temel birimi olan sinir hücrelerinin yapılarını ve görevlerini anlayabilmek için literatüre çeşitli sinir hücresi modelleri\r\nsunulmuştur (R. FitzHugh & J. Nagumo 1961, C. Morris & H. Lecar 1981, Matsuoka 1987, Izhikevich 2003). Hudgkin – Huxley\r\nSinir Hücresi Modeli, literatüre sunulan ilk modellerdendir ve sinir hücresinin kimyasal olarak sahip olduğu özellikleri daha\r\ndoğru bir şekilde ifade etmektedir (A. L. Hodgkin & A. F. Huxley 1952). Bu matematiksel model, dört adet lineer olmayan\r\ndenklemle ifade edilmektedir. Denklemlerin çokluğu, karmaşıklığı artırmakta bunun sonucu olarak matematiksel modelin\r\nanlaşılması güç hale gelmektedir. Öte yandan Izhikevich’ in modeli, Hudgkin-Huxley Sinir Hücresi Modeli gibi kimyasal\r\ndoğruluğa oldukça yakındır ve Hudgkin-Huxley Sinir Hücresi Modeli’ nden farklı olarak, matematiksel karmaşıklığı çok daha\r\nazdır. Bu çalışmada, Izhikevich’ in sunmuş olduğu matematiksel modelin MATLAB ortamında sayısal analizi yapılmış, ayrıca\r\nPython programı kullanılarak Raspberry Pi üzerinde fiziksel gerçekleştirimi tamamlanmıştır. Daha sonra sayısal analiz ile\r\nRaspberry Pi üzerindeki gerçekleştirim sonuçları karşılaştırılmıştır. Karşılaştırmalar sonucunda görülmüştür ki; standart bir\r\nyapıya sahip Raspberry Pi mini bilgisayarın da Izhikevich Sinir Hücresi Modeli’ nin gerçekleştiriminde çözüm olabilmektedir.  ", "language": "tr", "english_title": "Izhikevich Nerve Cell Model Implemented Using Raspberry Pi", "english_abstract": "Living things have a collection of nerve cells that produce rhythmic patterns called Central Pattern Generators (CPMs) to perform basic movements such as running, walking, and crawling. In addition to these basic movements, CPMs also perform rhythmic events such as heartbeat and respiration. Various nerve cell models have been presented in the literature to understand the structures and functions of nerve cells, the most basic unit of CPMs (R. FitzHugh & J. Nagumo 1961, C. Morris & H. Lecar 1981, Matsuoka 1987, Izhikevich 2003). The Hudgkin-Huxley Nerve Cell Model is one of the first models presented in the literature and more accurately expresses the chemical properties of the nerve cell (A. L. Hodgkin & A. F. Huxley 1952). This mathematical model is expressed with four nonlinear equations. The large number of equations increases the complexity, making the mathematical model difficult to understand. On the other hand, Izhikevich's model is quite close to chemical accuracy, like the Hudgkin-Huxley Neuron Model, and unlike the Hudgkin-Huxley Neuron Model, its mathematical complexity is much less. In this study, the numerical analysis of the mathematical model presented by Izhikevich was performed in the MATLAB environment, and its physical implementation on a Raspberry Pi was completed using the Python program. Then, the numerical analysis and the implementation results on the Raspberry Pi were compared. As a result of the comparisons, it was seen that a standard Raspberry Pi minicomputer can also provide a solution in the implementation of the Izhikevich Neuron Model.", "doi": null, "doi_url": null, "resource_url": "https://www.set-science.com/a/9cebae58-6e5b-4675-baf3-b919e62b7ff1", "pdf_url": "https://www.set-science.com/a/9cebae58-6e5b-4675-baf3-b919e62b7ff1/pdf", "og_image": "https://www.set-science.com/assets/img/og-card-v2.png", "authors": [ { "public_id": "53d9b9c0-a25b-4d7f-9fa4-65d758c65c94", "given": "Vedat Burak", "family": "Yücedağ", "full": "Vedat Burak Yücedağ", "literal": null, "orcid": null, "orcid_url": null, "email": "vedatburakyucedag@erciyes.edu.tr", "corresponding": true, "sequence": "first", "affiliations": [ { "name": "Erciyes University", "department": null, "country": null, "country_code": null, "ror": null, "ror_url": null } ] }, { "public_id": "8991848b-32ff-4865-92c7-bb6e7226d39a", "given": "İlker", "family": "Dalkıran", "full": "İlker Dalkıran", "literal": null, "orcid": null, "orcid_url": null, "email": "vedatburakyucedag@erciyes.edu.tr", "corresponding": false, "sequence": "additional", "affiliations": [ { "name": "Erciyes University", "department": null, "country": null, "country_code": null, "ror": null, "ror_url": null } ] } ], "keywords": [], "translations": [ { "language": "en", "title": "Izhikevich Nerve Cell Model Implemented Using Raspberry Pi", "abstract": "Living things have a collection of nerve cells that produce rhythmic patterns called Central Pattern Generators (CPMs) to perform basic movements such as running, walking, and crawling. In addition to these basic movements, CPMs also perform rhythmic events such as heartbeat and respiration. Various nerve cell models have been presented in the literature to understand the structures and functions of nerve cells, the most basic unit of CPMs (R. FitzHugh & J. Nagumo 1961, C. Morris & H. Lecar 1981, Matsuoka 1987, Izhikevich 2003). The Hudgkin-Huxley Nerve Cell Model is one of the first models presented in the literature and more accurately expresses the chemical properties of the nerve cell (A. L. Hodgkin & A. F. Huxley 1952). This mathematical model is expressed with four nonlinear equations. The large number of equations increases the complexity, making the mathematical model difficult to understand. On the other hand, Izhikevich's model is quite close to chemical accuracy, like the Hudgkin-Huxley Neuron Model, and unlike the Hudgkin-Huxley Neuron Model, its mathematical complexity is much less. In this study, the numerical analysis of the mathematical model presented by Izhikevich was performed in the MATLAB environment, and its physical implementation on a Raspberry Pi was completed using the Python program. Then, the numerical analysis and the implementation results on the Raspberry Pi were compared. As a result of the comparisons, it was seen that a standard Raspberry Pi minicomputer can also provide a solution in the implementation of the Izhikevich Neuron Model.", "keywords": [ "Merkezi Desen Üreteci, Izhikevich, Python, Raspberryi Pi, Sinir Hücresi Modeli" ] } ], "subjects": [], "subject_names": [], "published_date": "2018-12-31", "published_year": "2018", "received_date": null, "accepted_date": null, "volume": "3", "issue": null, "page_first": "1239", "page_last": "1241", "article_number": null, "conference": { "short_name": "ISAS2018-Winter", "full_title": "2nd International Symposium on Innovative Approaches in Scientific Studies", "name": "2nd International Symposium on Innovative Approaches in Scientific Studies", "acronym": "ISAS2018-Winter", "location": "Samsun, Türkiye", "start_date": "2018-11-30", "end_date": null, "public_id": "b8a703cc-09f7-4d5b-ae60-eb31a1050720" }, "proceedings": { "title": "2nd International Symposium on Innovative Approaches in Scientific Studies", "series_title": "Setsci conference indexing system", "publisher": "SET Technology", "isbn": null, "eisbn": null, "issn": "2618-6446", "eissn": null, "doi": null, "doi_prefix": null, "slug": "setsci-volume-3-2018-legacy-39", "public_id": "641f522e-9cf0-4c89-be17-c6037f3d1bca", "url": "https://www.set-science.com/p/setsci-volume-3-2018-legacy-39" }, "license": { "name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/", "code": "CC-BY-4.0", "open_access": true }, "copyright": { "holder": "SET Technology", "year": "2018" }, "funding_statement": null, "funders": [], "references": [ { "order": 1, "raw": "R. 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