Dataset for paper "Larger GPU-accelerated brain simulations with procedural connectivity"

Archives with filenames starting with 82d3c0816b0ad1c07ea27e61eb981f7a contain spike data from three 10.5 second "ground state" simulations of the model's "ground state" (chi=1.0)

Archives with filenames starting with b03fdaa1fd47a0e4a10483bc3901f1e5 contain spike data from three 100.5 second "ground state" simulations of the model's "resting state" (chi=1.9)

Simulating large spiking neural network models has, until now, needed so much memory for storing synaptic connections that it required high performance computer systems. Here, we present an alternative simulation method we call `procedural connectivity' where connectivity and synaptic weights are generated `on the fly' instead of stored and retrieved from memory. This method is particularly well-suited for use on Graphical Processing Units (GPUs) - which are a common fixture in many workstations. Extending our GeNN software with procedural connectivity and a second technical innovation for GPU code generation, we can simulate a recent model of the Macaque visual cortex with 4.13⁶ neurons and 24.2⁹ synapses on a single GPU - a significant step forward in making large-scale brain modelling accessible to more researchers."