Software for Brain Network Simulations: A Comparative Study
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.
SourceFrontiers In Neuroinformatics
Brain network simulators
Spiking neural networks