On modeling of a recurrent neural network from neural spiking data.

Özgür Doruk; Mohammed Al-akam

doi:10.53525/jster.999008

TR EN

Öz yinelemeli sinirsel ağların sinirsel atım verilerinden yararlanılarak modellenmesi hakkında

Öz

Gerçekçi uyaran-tepki verilerinden ateşleme hızına dayalı uyarıcı ve engelleyici sinir ağının kestirimini amaçlayan teorik ve hesaplamalı bir çalışma sunuyoruz. Uyaran ve tepki kayıtları, Diptera sınıfına mensup H1 nöronları üzerinde bir ölçüm gerçekleştiren önceki bir çalışmadan alınmıştır. Parametre tahmini, maksimum olabilirlik yöntemi ile yapılmaktadır. Uyaran-tepki verileri 20 dakikalık tek bir kayıt olduğu için bölümlere ayrılır ve istatistiksel bilgi içeriğini artırmak için ayrı bölümler üst üste bindirilir. Sentetik veri kullanmadığımız için model parametrelerinin gerçek değerleri bilinmemektedir. Bu nedenle, kaydedilen ve model yanıtlarının kesişme aralıklarını karşılaştırmak için iki örnekli Kolmogorov-Smirnov testi uygulanmıştır. Tahmin ve analiz sonuçları tablo ve grafik şeklinde sunulmaktadır. Ayrıca, değiştirilmiş bir Fitzhugh-Nagumo modelinin kullanıldığı önceki araştırmalarla bir karşılaştırma yapılmıştır.

Anahtar Kelimeler

Ateşleme hızı tabanlı sinirsel ağlar , uyarıcı-yatıştırıcı sinirsel ağlar , sinirsel atım , Kolmogorov-Smirnov Testi , atım arası zamanları

On modeling of a recurrent neural network from neural spiking data.

Abstract

We present a theoretical and computational work, aiming at the estimation of firing rate based excitatory and inhibitory neural network from realistic stimulus-response data. The stimulus and response recordings are taken from a previous study which performs a measurement on the H1 neurons of the order Diptera flies. The parameter estimation is performed by maximum likelihood method. As the stimulus-response data is a single recording of 20 minutes, it is segmented and individual segments are superimposed on each other to increase the statistical content of information. The true values of the model parameters are unknown as we are not using synthetic data. Because of this fact, two sample Kolmogorov-Smirnov test is applied to compare the interspiking intervals of the recorded and model responses. Estimation and analysis results are presented in tabular and graphical forms. In addition, a comparison with previous research employing a modified Fitzhugh-Nagumo model is made.

Keywords

: Firing rate based neural networks , Excitatory-inhibitory neurons , Neural spiking , Kolmogorov-Smirnov tests , Interspiking intervals

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Sinirbilim , Biyomedikal Mühendisliği , Elektrik Mühendisliği

Bölüm

Araştırma Makalesi

Yazarlar

Özgür Doruk ^*
0000-0002-9217-0845
Türkiye

Mohammed Al-akam Bu kişi benim
0000-0003-4774-2645
Iraq

Yayımlanma Tarihi

21 Aralık 2021

Gönderilme Tarihi

22 Eylül 2021

Kabul Tarihi

20 Ekim 2021

Yayımlandığı Sayı

Yıl 2021 Cilt: 2 Sayı: 2

DOI

https://doi.org/10.53525/jster.999008

IZ

https://izlik.org/JA37KW87LN

APA

Doruk, Ö., & Al-akam, M. (2021). On modeling of a recurrent neural network from neural spiking data. Journal of Science, Technology and Engineering Research, 2(2), 54-66. https://doi.org/10.53525/jster.999008

AMA

1.Doruk Ö, Al-akam M. On modeling of a recurrent neural network from neural spiking data. Journal of Science, Technology and Engineering Research. 2021;2(2):54-66. doi:10.53525/jster.999008

Chicago

Doruk, Özgür, ve Mohammed Al-akam. 2021. “On modeling of a recurrent neural network from neural spiking data”. Journal of Science, Technology and Engineering Research 2 (2): 54-66. https://doi.org/10.53525/jster.999008.

EndNote

Doruk Ö, Al-akam M (01 Aralık 2021) On modeling of a recurrent neural network from neural spiking data. Journal of Science, Technology and Engineering Research 2 2 54–66.

IEEE

[1]Ö. Doruk ve M. Al-akam, “On modeling of a recurrent neural network from neural spiking data”., Journal of Science, Technology and Engineering Research, c. 2, sy 2, ss. 54–66, Ara. 2021, doi: 10.53525/jster.999008.

ISNAD

Doruk, Özgür - Al-akam, Mohammed. “On modeling of a recurrent neural network from neural spiking data”. Journal of Science, Technology and Engineering Research 2/2 (01 Aralık 2021): 54-66. https://doi.org/10.53525/jster.999008.

JAMA

1.Doruk Ö, Al-akam M. On modeling of a recurrent neural network from neural spiking data. Journal of Science, Technology and Engineering Research. 2021;2:54–66.

MLA

Doruk, Özgür, ve Mohammed Al-akam. “On modeling of a recurrent neural network from neural spiking data”. Journal of Science, Technology and Engineering Research, c. 2, sy 2, Aralık 2021, ss. 54-66, doi:10.53525/jster.999008.

Vancouver

1.Özgür Doruk, Mohammed Al-akam. On modeling of a recurrent neural network from neural spiking data. Journal of Science, Technology and Engineering Research. 01 Aralık 2021;2(2):54-66. doi:10.53525/jster.999008

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Öz yinelemeli sinirsel ağların sinirsel atım verilerinden yararlanılarak modellenmesi hakkında

Öz

Anahtar Kelimeler

On modeling of a recurrent neural network from neural spiking data.

Abstract

Keywords

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster