Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Impact of Correlated Inputs on the Output of the Integrate-and-Fire Model
Neural Computation
Generation of Synthetic Spike Trains with Defined Pairwise Correlations
Neural Computation
Conditional mixture model for correlated neuronal spikes
Neural Computation
CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains
Journal of Computational Neuroscience
A new method to infer higher-order spike correlations from membrane potentials
Journal of Computational Neuroscience
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Neuronal spike trains display correlations at diverse timescales throughout the nervous system. The functional significance of these correlations is largely unknown, and computational investigations can help us understand their role. In order to generate correlated spike trains with given statistics, several case-specific methods have been described in the litterature. This letter presents two general methods to generate sets of spike trains with given firing rates and pairwise correlation functions, along with efficient simulation algorithms.