  Simulation speed matters for neuroscientific research: this includes not only
how quickly the simulated model time of a large-scale spiking neuronal network
progresses, but also how long it takes to instantiate the network model in
computer memory. On the hardware side, acceleration via highly parallel GPUs is
being increasingly utilized. On the software side, code generation approaches
ensure highly optimized code, at the expense of repeated code regeneration and
recompilation after modifications to the network model. Aiming for a greater
flexibility with respect to iterative model changes, here we propose a new
method for creating network connections interactively, dynamically, and
directly in GPU memory through a set of commonly used high-level connection
rules. We validate the simulation performance with both consumer and data
center GPUs on two neuroscientifically relevant models: a cortical microcircuit
of about 77,000 leaky-integrate-and-fire neuron models and 300 million static
synapses, and a two-population network recurrently connected using a variety of
connection rules. With our proposed ad hoc network instantiation, both network
construction and simulation times are comparable or shorter than those obtained
with other state-of-the-art simulation technologies, while still meeting the
flexibility demands of explorative network modeling.
