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Accelerating Event Based Simulation for Multi-synapse Spiking Neural Networks

The simulation of large spiking neural networks (SNN) is still a very time consuming task. Therefore most simulations are limited to rather unrealistic small or medium sized networks (typically hundreds of neurons). In this paper, some methods for the fast simulation of large SNN are discussed. Our results equally amongst others show that event based simulation is an efficient way of simulating SNN, although not all neuron models are suited for an event based approach. We compare some models and discuss several techniques for accelerating the simulation of more complex models. Finally we present an algorithm that is able to handle multi-synapse models efficiently

D'Haene, Michiel

Schrauwen, Benjamin

Stroobandt, Dirk

Ghent University Academic Bibliography

Accelerating event based simulationfor multi-synapse spiking neural networksMichiel D’Haene?, Benjamin Schrauwen??, and Dirk StroobandtGhent University, Department of Electronics and Information Systems, Ghent, BelgiumfMichiel.DHaene, Benjamin.Schrauwen, Dirk Stroobandtg@ugent.behttp://www.elis.ugent.be/SNNAbstract. The simulation of large spiking neural networks (SNN) is still a verytime consuming task. Therefore most simulations are limited to rather unrealisticsmall or medium sized networks (typically hundreds of neurons). In this paper,some methods for the fast simulation of large SNN are discussed. Our resultsequally amongst others show that event based simulation is an efficient way ofsimulating SNN, although not all neuron models are suited for an event based ap-proach. We compare some models and discuss several techniques for acceleratingthe simulation of more complex models. Finally we present an algorithm that isable to handle multi-synapse models efficiently.1 IntroductionDespite the ever increasing computational power in computer systems, there is still ahuge demand for more computational power, especially in the field of spiking neuralnetworks (SNN). Spiking neurons are biologically inspired neurons that communicateby using spikes. Because here the timing of the spikes is considered, SNN are able tohandle temporal problems more efficiently (for example speech recognition [18]) andhave more computational power than artificial neural networks [9] which use the aver-age firing rate of neurons as inputs. Furthermore, they communicate through discretespikes instead of analog values which significantly reduces the communication costsbetween neurons. This makes them particularly better suited for hardware implementa-tions [13][14].The behaviour of a spiking neuron can be represented by an internal membranepotential which is influenced by incoming spikes. When the potential of the membranereaches a certain threshold value, the membrane potential will be reset to a lower valueand a spike is emitted. It is important to note that each neuron operates independently,except when a spike is communicated between neurons.An obvious way of implementing SNN in hardware is a one to one placement ofthe neurons into physical components. This approach benefits from the inherent paral-lel nature of spiking neural networks and allows extremely fast simulations (orders of? Michiel D’Haene is sponsored by the Institute for the Promotion of Innovationthrough Science and Technology in Flanders (IWT-Vlaanderen)?? Benjamin Schrauwen is funded by FWO Flanders project G.0317.05The remainder of this paper is not in-cluded as this paper is copyrighted ma-terial. If you wish to obtain an elec-tronic version of this paper, please sendan email to bib@elis.UGent.be with arequest for publication P106.143.pdf.1

Accelerating event based simulation for multi-synapse spiking neural networks

https://biblio.ugent.be/publication/374637/file/496475

Accelerating event based simulation for multi-synapse spiking neural networks

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Ghent University Academic Bibliography