Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Generation of Synthetic Spike Trains with Defined Pairwise Correlations
Neural Computation
Neural Computation
Can spike coordination be differentiated from rate covariation?
Neural Computation
Generation of correlated spike trains
Neural Computation
Generating spike trains with specified correlation coefficients
Neural Computation
Multivariate autoregressive modeling and granger causality analysis of multiple spike trains
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Correlation-distortion based identification of Linear-Nonlinear-Poisson models
Journal of Computational Neuroscience
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
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Emerging evidence indicates that information processing, as well as learning and memory processes, in both the network and single-neuron levels are highly dependent on the correlation structure of multiple spike trains. Contemporary experimental as well as theoretical studies that involve quasi-realistic neuronal stimulation thus require a method for controlling spike train correlations. This letter introduces a general new strategy for generating multiple spike trains with exactly controlled mean firing rates and correlation structure (defined in terms of auto-and cross-correlation functions). Our approach nonlinearly transforms random gaussian-distributed processes with a predistorted correlation structure into nonnegative rate processes, which are then used to generate doubly stochastic Poisson point processes with the required correlation structure. We show how this approach can be used to generate stationary or nonstationary spike trains from small or large groups of neurons with diverse auto-and cross-correlation structures. We analyze and derive analytical formulas for the high-order correlation structure of generated spike trains and discuss the limitations of this approach.