Synchronization of pulse-coupled biological oscillators
SIAM Journal on Applied Mathematics
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On numerical simulations of integrate-and-fire neural networks
Neural Computation
Parallel and Distribution Simulation Systems
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Event-driven simulation of spiking neurons with stochastic dynamics
Neural Computation
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Exact simulation of integrate-and-fire models with synaptic conductances
Neural Computation
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Neural Computation
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Neural Computation
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Neural Computation
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Neural Computation
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Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Vectorized algorithms for spiking neural network simulation
Neural Computation
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Journal of Computational Neuroscience
Spiking neural network simulation: memory-optimal synaptic event scheduling
Journal of Computational Neuroscience
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Journal of Computational Neuroscience
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Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
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Very large networks of spiking neurons can be simulated efficiently in parallel under the constraint that spike times are bound to an equidistant time grid. Within this scheme, the subthreshold dynamics of a wide class of integrate-and-fire-type neuron models can be integrated exactly from one grid point to the next. However, the loss in accuracy caused by restricting spike times to the grid can have undesirable consequences, which has led to interest in interpolating spike times between the grid points to retrieve an adequate representation of network dynamics. We demonstrate that the exact integration scheme can be combined naturally with off-grid spike events found by interpolation. We show that by exploiting the existence of a minimal synaptic propagation delay, the need for a central event queue is removed, so that the precision of event-driven simulation on the level of single neurons is combined with the efficiency of time-driven global scheduling. Further, for neuron models with linear subthreshold dynamics, even local event queuing can be avoided, resulting in much greater efficiency on the single-neuron level. These ideas are exemplified by two implementations of a widely used neuron model. We present a measure for the efficiency of network simulations in terms of their integration error and show that for a wide range of input spike rates, the novel techniques we present are both more accurate and faster than standard techniques.