Algorithms & data structures
Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
Skip lists: a probabilistic alternative to balanced trees
Communications of the ACM
Fast calculation of synaptic conductances
Neural Computation
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Optimizing synaptic conductance calculation for network simulations
Neural Computation
Parallel Event-Driven Neural Network Simulations Using the Hodgkin-Huxley Neuron Model
Proceedings of the 19th Workshop on Principles of Advanced and Distributed Simulation
A distributed and multithreaded neural event driven simulation framework
PDCN'06 Proceedings of the 24th IASTED international conference on Parallel and distributed computing and networks
Spiking neural nets with symbolic internal state
Information Processing Letters - Special issue on applications of spiking neural networks
How much can we trust neural simulation strategies?
Neurocomputing
Spiking neural nets with symbolic internal state
Information Processing Letters - Special issue on applications of spiking neural networks
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
Spiking neurons computing platform
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Coarse-grained event tree analysis for quantifying Hodgkin-Huxley neuronal network dynamics
Journal of Computational Neuroscience
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We present a new technique, based on a proposed event-based strategy (Mattia & Del Giudice, 2000), for efficiently simulating large networks of simple model neurons. The strategy was based on the fact that interactions among neurons occur by means of events that are well localized in time (the action potentials) and relatively rare. In the interval between two of these events, the state variables associated with a model neuron or a synapse evolved deterministically and in a predictable way. Here, we extend the event-driven simulation strategy to the case in which the dynamics of the state variables in the inter-event intervals are stochastic. This extension captures both the situation in which the simulated neurons are inherently noisy and the case in which they are embedded in a very large network and receive a huge number of random synaptic inputs. We show how to effectively include the impact of large background populations into neuronal dynamics by means of the numerical evaluation of the statistical properties of single-model neurons under random current injection. The new simulation strategy allows the study of networks of interacting neurons with an arbitrary number of external afferents and inherent stochastic dynamics.