Fitting of Dynamic Recurrent Neural Network Models To Sensory Stimulus-Response Data

dc.authorid Doruk, Ozgur/0000-0002-9217-0845
dc.authorscopusid 8503734100
dc.authorscopusid 7404451228
dc.authorwosid Doruk, Ozgur/T-9951-2018
dc.contributor.author Doruk, R. Ozgur
dc.contributor.author Zhang, Kechen
dc.contributor.other Electrical-Electronics Engineering
dc.date.accessioned 2024-07-05T15:26:51Z
dc.date.available 2024-07-05T15:26:51Z
dc.date.issued 2018
dc.department Atılım University en_US
dc.department-temp [Doruk, R. Ozgur] Atilim Univ, Dept Elect & Elect Engn, Kizilcasar Mahallesi, TR-06836 Ankara, Turkey; [Zhang, Kechen] Johns Hopkins Sch Med, Dept Biomed Engn, 720 Rutland Ave,Traylor 407, Baltimore, MD 21205 USA en_US
dc.description Doruk, Ozgur/0000-0002-9217-0845 en_US
dc.description.abstract We present a theoretical study aiming at model fitting for sensory neurons. Conventional neural network training approaches are not applicable to this problem due to lack of continuous data. Although the stimulus can be considered as a smooth time-dependent variable, the associated response will be a set of neural spike timings (roughly the instants of successive action potential peaks) that have no amplitude information. A recurrent neural network model can be fitted to such a stimulus-response data pair by using the maximum likelihood estimation method where the likelihood function is derived from Poisson statistics of neural spiking. The universal approximation feature of the recurrent dynamical neuron network models allows us to describe excitatory-inhibitory characteristics of an actual sensory neural network with any desired number of neurons. The stimulus data are generated by a phased cosine Fourier series having a fixed amplitude and frequency but a randomly shot phase. Various values of amplitude, stimulus component size, and sample size are applied in order to examine the effect of the stimulus to the identification process. Results are presented in tabular and graphical forms at the end of this text. In addition, to demonstrate the success of this research, a study involving the same model, nominal parameters and stimulus structure, and another study that works on different models are compared to that of this research. en_US
dc.description.sponsorship Turkish Scientific and Technological Research Council [DB-2219] en_US
dc.description.sponsorship This study was partially supported by the Turkish Scientific and Technological Research Council's DB-2219 Grant Program. en_US
dc.identifier.citationcount 6
dc.identifier.doi 10.1007/s10867-018-9501-z
dc.identifier.endpage 469 en_US
dc.identifier.issn 0092-0606
dc.identifier.issn 1573-0689
dc.identifier.issue 3 en_US
dc.identifier.pmid 29860641
dc.identifier.scopus 2-s2.0-85047961220
dc.identifier.startpage 449 en_US
dc.identifier.uri https://doi.org/10.1007/s10867-018-9501-z
dc.identifier.uri https://hdl.handle.net/20.500.14411/2611
dc.identifier.volume 44 en_US
dc.identifier.wos WOS:000441201500012
dc.identifier.wosquality Q4
dc.institutionauthor Doruk, Reşat Özgür
dc.language.iso en en_US
dc.publisher Springer en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 6
dc.subject Sensory neurons en_US
dc.subject Recurrent neural network en_US
dc.subject Excitatory neuron en_US
dc.subject Inhibitory neuron en_US
dc.subject Neural spiking en_US
dc.subject Maximum likelihood estimation en_US
dc.title Fitting of Dynamic Recurrent Neural Network Models To Sensory Stimulus-Response Data en_US
dc.type Article en_US
dc.wos.citedbyCount 6
dspace.entity.type Publication
relation.isAuthorOfPublication bbc93c72-5a45-4b28-9b05-5ae035e52a76
relation.isAuthorOfPublication.latestForDiscovery bbc93c72-5a45-4b28-9b05-5ae035e52a76
relation.isOrgUnitOfPublication 032f8aca-54a7-476c-b399-6f26feb20a7d
relation.isOrgUnitOfPublication.latestForDiscovery 032f8aca-54a7-476c-b399-6f26feb20a7d

Files

Collections