Fitting a Recurrent Dynamical Neural Network To Neural Spiking Data: Tackling the Sigmoidal Gain Function Issues

dc.authorid Doruk, Ozgur/0000-0002-9217-0845
dc.authorscopusid 8503734100
dc.authorwosid Doruk, Ozgur/T-9951-2018
dc.contributor.author Doruk, Reşat Özgür
dc.contributor.other Electrical-Electronics Engineering
dc.date.accessioned 2024-07-05T15:28:41Z
dc.date.available 2024-07-05T15:28:41Z
dc.date.issued 2019
dc.department Atılım University en_US
dc.department-temp ATILIM ÜNİVERSİTESİ en_US
dc.description Doruk, Ozgur/0000-0002-9217-0845 en_US
dc.description.abstract This is a continuation of a recent study (Doruk RO, Zhang K. Fitting of dynamic recurrent neural networkmodels to sensory stimulus-response data. J Biol Phys 2018; 44: 449-469), where a continuous time dynamical recurrentneural network is fitted to neural spiking data. In this research, we address the issues arising from the inclusion ofsigmoidal gain function parameters to the estimation algorithm. The neural spiking data will be obtained from the samemodel as that of Doruk and Zhang, but we propose a different model for identification. This will also be a continuoustime recurrent neural network, but with generic sigmoidal gains. The simulation framework and estimation algorithmsare kept similar to that of Doruk and Zhang so that we can have a solid base to compare the results. We evaluatethe estimation performance in two different ways. First, we compare the firing rate responses of the original and theestimated model. We find that responses of both models to the same stimuli are similar. Secondly, we evaluate variationsof the standard deviations of the estimates against a number of samples and stimulus parameters. They show a similarpattern to that of Doruk and Zhang. We thus conclude that our model serves as a reasonable alternative provided thatfiring rate is the response of interest (to any stimulus). en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.3906/elk-1808-29
dc.identifier.endpage 920 en_US
dc.identifier.issn 1300-0632
dc.identifier.issn 1300-0632
dc.identifier.issue 2 en_US
dc.identifier.scopus 2-s2.0-85065831717
dc.identifier.scopusquality Q3
dc.identifier.startpage 903 en_US
dc.identifier.trdizinid 336529
dc.identifier.uri https://doi.org/10.3906/elk-1808-29
dc.identifier.uri https://search.trdizin.gov.tr/tr/yayin/detay/336529/fitting-a-recurrent-dynamical-neural-network-to-neural-spiking-data-tackling-the-sigmoidal-gain-function-issues
dc.identifier.volume 27 en_US
dc.identifier.wos WOS:000463355800017
dc.identifier.wosquality Q4
dc.institutionauthor Doruk, Reşat Özgür
dc.language.iso en en_US
dc.publisher Tubitak Scientific & Technological Research Council Turkey en_US
dc.relation.ispartof Turkish Journal of Electrical Engineering and Computer Sciences en_US
dc.relation.publicationcategory Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 0
dc.subject Mühendislik en_US
dc.subject Elektrik ve Elektronik en_US
dc.subject Bilgisayar Bilimleri en_US
dc.subject Yazılım Mühendisliği en_US
dc.subject Bilgisayar Bilimleri en_US
dc.subject Sibernitik en_US
dc.subject Bilgisayar Bilimleri en_US
dc.subject Bilgi Sistemleri en_US
dc.subject Bilgisayar Bilimleri en_US
dc.subject Donanım ve Mimari en_US
dc.subject Bilgisayar Bilimleri en_US
dc.subject Teori ve Metotlar en_US
dc.subject Bilgisayar Bilimleri en_US
dc.subject Yapay Zeka en_US
dc.title Fitting a Recurrent Dynamical Neural Network To Neural Spiking Data: Tackling the Sigmoidal Gain Function Issues en_US
dc.type Article en_US
dc.wos.citedbyCount 0
dspace.entity.type Publication
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