Runtime Network Construction on GPU Devices for Large-Scale Spiking Models Archive

Simulation speed matters for neuroscientific research: this includes not only how fast
the simulated model time of a large-scale spiking neuronal network progresses, but also how long
it takes beforehand 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, yet on the cost of repeated code regeneration
and recompilation after modifications to the network model. Aiming for a greater flexibility with
respect to iterative model changes, we here 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 balanced random network of excitatory and
inhibitory Izhikevich neuron models interconnected with synapses using spike-timing-dependent
plasticity. With the proposed ad hoc network construction, both instantiation and simulation times are
comparable or even shorter than those obtained with other state-of-the-art simulation technologies,
while meeting the flexibility demands of explorative network modeling.