NeuroXyce: a highly parallelized simulator for biologically realistic neural networks
Christina Warrender (Sandia National Laboratories), James Aimone (Sandia National Laboratories), Corinne Teeter (Sandia National Laboratories), Richard Schiek (Sandia National Laboratories)
The increasing availability of high performance computing platforms, either through supercomputers or cloud computing, offers tremendous potential to computational neuroscientists interested in simulating biologically realistic networks at large scales. Unfortunately, tools that take full advantage of these platforms have been slow to develop, and the parallelization of neural simulations represents a non-trivial amount of work. In current network simulators the parallelization scheme is often specified by the user. This specification can be quite arduous and often the user is uninformed of which scheme is optimal. This is noteworthy since parallelization techniques can substantially influence the run time of large-scale neural network simulations, and a poorly parallelized model may offer little or no advantage over conventional approaches. We have created a simulator capable of simulating multicompartment, branched neurons with ion channels by building on the previously existing Xyce parallel electronic circuit simulator (xyce.sandia.gov). NeuroXyce uses advanced parallel integration and solver methods, and automatically handles load balancing among multiple processors, removing this burden from the user.
Here we demonstrate the scalability of NeuroXyce and compare the simulation run time and ease of use with other popular simulators (i.e. NEURON). Our simulation paradigm consists of a network of 80 percent excitatory neurons and 20 percent inhibitory neurons. Neurons have Hodgkin-Huxley sodium and potassium channel dynamics. The neurons are randomly connected with a probability of 0.02. The strength of the synapses scales depending on the size of the network (10,000 to 1,000,000 neurons). The excitatory connections simulate AMPA synapses and the inhibitory connections simulate GABA synapses. We measure simulation run time and network dynamics as the size of the network is increased.
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Large scale modeling