Software for Brain Network Simulations: A Comparative Study

dc.authorscopusid55173951700
dc.authorscopusid35311455000
dc.authorscopusid6601990115
dc.authorscopusid6701472736
dc.contributor.authorBozkuş, Zeki
dc.contributor.authorNarayana, Vikram
dc.contributor.authorBozkus, Zeki
dc.contributor.authorEl-Ghazawi, Tarek A.
dc.date.accessioned2024-10-15T19:39:34Z
dc.date.available2024-10-15T19:39:34Z
dc.date.issued2017
dc.departmentKadir Has Universityen_US
dc.department-temp[Tikidji-Hamburyan, Ruben A.; Narayana, Vikram; El-Ghazawi, Tarek A.] George Washington Univ, Sch Engn & Appl Sci, Washington, DC 20052 USA; [Bozkus, Zeki] Kadir Has Univ, Comp Engn Dept, Istanbul, Turkeyen_US
dc.description.abstractNumerical simulations of brain networks are a critical part of our efforts in understanding brain functions under pathological and normal conditions. For several decades, the community has developed many software packages and simulators to accelerate research in computational neuroscience. In this article, we select the three most popular simulators, as determined by the number of models in the ModelDB database, such as NEURON, GENESIS, and BRIAN, and perform an independent evaluation of these simulators. In addition, we study NEST, one of the lead simulators of the Human Brain Project. First, we study them based on one of the most important characteristics, the range of supported models. Our investigation reveals that brain network simulators may be biased toward supporting a specific set of models. However, all simulators tend to expand the supported range of models by providing a universal environment for the computational study of individual neurons and brain networks. Next, our investigations on the characteristics of computational architecture and efficiency indicate that all simulators compile the most computationally intensive procedures into binary code, with the aim of maximizing their computational performance. However, not all simulators provide the simplest method for module development and/or guarantee efficient binary code. Third, a study of their amenability for high-performance computing reveals that NEST can almost transparently map an existing model on a cluster or multicore computer, while NEURON requires code modification if the model developed for a single computer has to be mapped on a computational cluster. Interestingly, parallelization is the weakest characteristic of BRIAN, which provides no support for cluster computations and limited support for multicore computers. Fourth, we identify the level of user support and frequency of usage for all simulators. Finally, we carry out an evaluation using two case studies: a large network with simplified neural and synaptic models and a small network with detailed models. These two case studies allow us to avoid any bias toward a particular software package. The results indicate that BRIAN provides the most concise language for both cases considered. Furthermore, as expected, NEST mostly favors large network models, while NEURON is better suited for detailed models. Overall, the case studies reinforce our general observation that simulators have a bias in the computational performance toward specific types of the brain network models.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [114E046]en_US
dc.description.sponsorshipZeki Bozkus is funded by Scientific and Technological Research Council of Turkey (TUBITAK; 114E046).en_US
dc.description.woscitationindexScience Citation Index Expanded
dc.identifier.citation42
dc.identifier.doi10.3389/fninf.2017.00046
dc.identifier.issn1662-5196
dc.identifier.pmid28775687
dc.identifier.scopus2-s2.0-85028071516
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3389/fninf.2017.00046
dc.identifier.urihttps://hdl.handle.net/20.500.12469/6330
dc.identifier.volume11en_US
dc.identifier.wosWOS:000406561700001
dc.identifier.wosqualityQ2
dc.language.isoenen_US
dc.publisherFrontiers Media Saen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectcomputational neuroscienceen_US
dc.subjectbrain network simulatorsen_US
dc.subjectspiking neural networksen_US
dc.subjectcomparative studyen_US
dc.subjectphenomenological modelen_US
dc.subjectconductance-based modelen_US
dc.titleSoftware for Brain Network Simulations: A Comparative Studyen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublication14914cc2-2a09-46be-a429-12ef3a6f5456
relation.isAuthorOfPublication.latestForDiscovery14914cc2-2a09-46be-a429-12ef3a6f5456

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