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NeMo: a platform for neural modelling of spiking neurons using GPUs

Fidjeland, A. K., Roesch, E. B. ORCID:, Shanahan, M. P. and Luk, W. (2009) NeMo: a platform for neural modelling of spiking neurons using GPUs. In: 20th IEEE International Conference on Application-specific Systems, Architectures and Processors, 2009. ASAP 2009. IEEE, pp. 137-144. ISBN 9780769537320

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To link to this item DOI: 10.1109/ASAP.2009.24


Simulating spiking neural networks is of great interest to scientists wanting to model the functioning of the brain. However, large-scale models are expensive to simulate due to the number and interconnectedness of neurons in the brain. Furthermore, where such simulations are used in an embodied setting, the simulation must be real-time in order to be useful. In this paper we present NeMo, a platform for such simulations which achieves high performance through the use of highly parallel commodity hardware in the form of graphics processing units (GPUs). NeMo makes use of the Izhikevich neuron model which provides a range of realistic spiking dynamics while being computationally efficient. Our GPU kernel can deliver up to 400 million spikes per second. This corresponds to a real-time simulation of around 40 000 neurons under biologically plausible conditions with 1000 synapses per neuron and a mean firing rate of 10 Hz.

Item Type:Book or Report Section
Divisions:Interdisciplinary Research Centres (IDRCs) > Centre for Integrative Neuroscience and Neurodynamics (CINN)
ID Code:30301
Uncontrolled Keywords:GPU, spiking neural networks

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