The effect of a refractory period on the power spectrum of neuronal discharge
SIAM Journal on Applied Mathematics
Information-geometric measure for neural spikes
Neural Computation
Correlated inhibitory and excitatory inputs to the coincidence detector: analytical solution
IEEE Transactions on Neural Networks
Generation of correlated spike trains
Neural Computation
Generating spike trains with specified correlation coefficients
Neural Computation
Measure of correlation orthogonal to change in firing rate
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
Applying the multivariate time-rescaling theorem to neural population models
Neural Computation
Comparative study of approximate entropy and sample entropy robustness to spikes
Artificial Intelligence in Medicine
Impact of correlated neural activity on decision-making performance
Neural Computation
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Recent technological advances as well as progress in theoretical understanding of neural systems have created a need for synthetic spike trains with controlled mean rate and pairwise cross-correlation. This report introduces and analyzes a novel algorithm for the generation of discretized spike trains with arbitrary mean rates and controlled cross correlation. Pairs of spike trains with any pairwise correlation can be generated, and higher-order correlations are compatible with common synaptic input. Relations between allowable mean rates and correlations within a population are discussed. The algorithm is highly efficient, its complexity increasing linearly with the number of spike trains generated and therefore inversely with the number of cross-correlated pairs.