AbstractLarge-scale simulations of spiking neural network models are an important tool for improving our understanding of the dynamics and ultimately the function of brains. However, even small mammals such as mice have on the order of 1 × 1012 synaptic connections which, in simulations, are each typically charaterized by at least one floating-point value. This amounts to several terabytes of data – an unrealistic memory requirement for a single desktop machine. Large models are therefore typically simulated on distributed supercomputers which is costly and limits large-scale modelling to a few privileged research groups. In this work, we describe extensions to GeNN – our Graphical Processing Unit (GPU) accelerated spiking neural network simulator – that enable it to ‘procedurally’ generate connectivity and synaptic weights ‘on the go’ as spikes are triggered, instead of storing and retrieving them from memory. We find that GPUs are well-suited to this approach because of their raw computational power which, due to memory bandwidth limitations, is often under-utilised when simulating spiking neural networks. We demonstrate the value of our approach with a recent model of the Macaque visual cortex consisting of 4.13 × 106 neurons and 24.2 × 109 synapses. Using our new method, it can be simulated on a single GPU – a significant step forward in making large-scale brain modelling accessible to many more researchers. Our results match those obtained on a supercomputer and the simulation runs up to 35 % faster on a single high-end GPU than previously on over 1000 supercomputer nodes.
On the Applicability of Spiking Neural Network Models to Solve the Task of Recognizing Gender Hidden in Texts