Grbatinić, Ivan (36767451200)Ivan (36767451200)GrbatinićRajković, Nemanja (55844172600)Nemanja (55844172600)RajkovićMilošević, Nebojša (35608832100)Nebojša (35608832100)Milošević2025-07-022025-07-022018https://doi.org/10.1080/21681163.2016.1160798https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041072939&doi=10.1080%2f21681163.2016.1160798&partnerID=40&md5=d9ccd1a1ec542746a36b3eb5ff12951ehttps://remedy.med.bg.ac.rs/handle/123456789/13044Backgrounds: The aim of this study is to model 2D dentate nucleus neuron surface (2D DNNIS) using the RSM modelling method and show in general such a modelling approach. Additionally, an application is made to the neurons of dentate nucleus lamina, namely VLL and DML. Methods: response surface methodology (RSM). Results: Several modelling formula are obtained. Models are classified according to neuron samples on which are obtained and number of factors used. Thus, in general, constrained and non-constrained, VLL and DML models are analysed. Obtained non-constrained models are quadratic model with multifactor interaction for all samples (adjusted R20.96) and VLL sample (adjusted R20.98) and linear model with multifactor interaction for DML sample (adjusted R20.95). Constrained models are bifactor models, namely general one without factor interaction with adjusted R20.93; and for particular lamina, the models are accompanied with factor interaction (adjusted R20.95). Conclusion: Though it is of the smallest adjusted R2(0.93), constrained general model is shown to be the most promising one for modelling 2D neuron surface for adult DNN. © 2016 Informa UK Limited, trading as Taylor & Francis Group.2D binary dentate nucleus neuron surfacecomputational modellingRSM