Year 2018 😗

State-of-the-art software tools for neuronal network simulations scale to the largest computing systems available today and enable investigations of large-scale networks of up to 10% of the human cortex at a resolution of individual neurons and synapses. Due to an upper limit on the number of incoming connections of a single neuron, network connectivity becomes extremely sparse at this scale. To manage computational costs, simulation software ultimately targeting the brain scale needs to fully exploit this sparsity. Here we present a two-tier connection infrastructure and a framework for directed communication among compute nodes accounting for the sparsity of brain-scale networks. We demonstrate the feasibility of this approach by implementing the technology in the NEST simulation code and we investigate its performance in different scaling scenarios of typical network simulations. Our results show that the new data structures and communication scheme prepare the simulation kernel for post-petascale high-performance computing facilities without sacrificing performance in smaller systems.

Modern neuroscience has established numerical simulation as a third pillar supporting the investigation of the dynamics and function of neuronal networks, next to experimental and theoretical approaches. Simulation software reflects the diversity of modern neuroscientific research with tools ranging from the molecular scale to investigate processes at individual synapses (Wils and De Schutter, 2009) to whole-brain simulations at the population level that can be directly related to clinical measures (Sanz Leon et al., 2013). Most neuronal network simulation software, however, is based on the hypothesis that the main processes of brain function can be captured at the level of individual nerve cells and their interactions through electrical pulses. Since these pulses show little variation in shape, it is generally believed that they convey information only through their timing or rate of occurrence.