Skip lists: a probabilistic alternative to balanced trees
Communications of the ACM
Introduction to algorithms
The NEURON simulation environment
Neural Computation
Trust-region methods
Performance of height-balanced trees
Communications of the ACM
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Event-driven simulation of spiking neurons with stochastic dynamics
Neural Computation
Neural Computation
Real-time spiking neural network: an adaptive cerebellar model
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Anatomy of a cortical simulator
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Visual processing platform based on artificial retinas
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Spiking neural network simulation: memory-optimal synaptic event scheduling
Journal of Computational Neuroscience
Context separability mediated by the granular layer in a spiking cerebellum model for robot control
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
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
Lookup table powered neural event-driven simulator
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.