Calendar queues: a fast 0(1) priority queue implementation for the simulation event set problem
Communications of the ACM
On numerical simulations of integrate-and-fire neural networks
Neural Computation
Computing and learning with dynamic synapses
Pulsed neural networks
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Synapses as dynamic memory buffers
Neural Networks
Exact simulation of integrate-and-fire models with synaptic conductances
Neural Computation
Isolated word recognition with the liquid state machine: a case study
Information Processing Letters - Special issue on applications of spiking neural networks
Exact Simulation of Integrate-and-Fire Models with Exponential Currents
Neural Computation
Event-driven simulations of nonlinear integrate-and-fire neurons
Neural Computation
Lower bounds for the computational power of networks of spiking neurons
Neural Computation
Accelerating event based simulation for multi-synapse spiking neural networks
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Lookup table powered neural event-driven simulator
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Fast and exact simulation methods applied on a broad range of neuron models
Neural Computation
Event and time driven hybrid simulation of spiking neural networks
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
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The simulation of spiking neural networks (SNNs) is known to be a very time-consuming task. This limits the size of SNN that can be simulated in reasonable time or forces users to overly limit the complexity of the neuron models. This is one of the driving forces behind much of the recent research on event-driven simulation strategies. Although event-driven simulation allows precise and efficient simulation of certain spiking neuron models, it is not straightforward to generalize the technique to more complex neuron models, mostly because the firing time of these neuron models is computationally expensive to evaluate. Most solutions proposed in literature concentrate on algorithms that can solve this problem efficiently. However, these solutions do not scale well when more state variables are involved in the neuron model, which is, for example, the case when multiple synaptic time constants for each neuron are used. In this letter, we show that an exact prediction of the firing time is not required in order to guarantee exact simulation results. Several techniques are presented that try to do the least possible amount of work to predict the firing times. We propose an elegant algorithm for the simulation of leaky integrate-and-fire (LIF) neurons with an arbitrary number of (unconstrained) synaptic time constants, which is able to combine these algorithmic techniques efficiently, resulting in very high simulation speed. Moreover, our algorithm is highly independent of the complexity (i.e., number of synaptic time constants) of the underlying neuron model.