Software for Brain Network Simulations: A Comparative Study

dc.authorscopusid 55173951700
dc.authorscopusid 35311455000
dc.authorscopusid 6601990115
dc.authorscopusid 6701472736
dc.contributor.author Bozkuş, Zeki
dc.contributor.author Narayana, Vikram
dc.contributor.author Bozkus, Zeki
dc.contributor.author El-Ghazawi, Tarek A.
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-10-15T19:39:34Z
dc.date.available 2024-10-15T19:39:34Z
dc.date.issued 2017
dc.department Kadir Has University en_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, Turkey en_US
dc.description.abstract Numerical 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.sponsorship Scientific and Technological Research Council of Turkey (TUBITAK) [114E046] en_US
dc.description.sponsorship Zeki Bozkus is funded by Scientific and Technological Research Council of Turkey (TUBITAK; 114E046). en_US
dc.description.woscitationindex Science Citation Index Expanded
dc.identifier.citationcount 42
dc.identifier.doi 10.3389/fninf.2017.00046
dc.identifier.issn 1662-5196
dc.identifier.pmid 28775687
dc.identifier.scopus 2-s2.0-85028071516
dc.identifier.scopusquality Q2
dc.identifier.uri https://doi.org/10.3389/fninf.2017.00046
dc.identifier.uri https://hdl.handle.net/20.500.12469/6330
dc.identifier.volume 11 en_US
dc.identifier.wos WOS:000406561700001
dc.identifier.wosquality Q2
dc.language.iso en en_US
dc.publisher Frontiers Media Sa 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 44
dc.subject computational neuroscience en_US
dc.subject brain network simulators en_US
dc.subject spiking neural networks en_US
dc.subject comparative study en_US
dc.subject phenomenological model en_US
dc.subject conductance-based model en_US
dc.title Software for Brain Network Simulations: A Comparative Study en_US
dc.type Article en_US
dc.wos.citedbyCount 43
dspace.entity.type Publication
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