The effect of correlated variability on the accuracy of a population code
Neural Computation
On different facets of regularization theory
Neural Computation
Behavior of the NORTA method for correlated random vector generation as the dimension increases
ACM Transactions on Modeling and Computer Simulation (TOMACS)
The Shape of Neural Dependence
Neural Computation
Generation of Synthetic Spike Trains with Defined Pairwise Correlations
Neural Computation
Information geometry on hierarchy of probability distributions
IEEE Transactions on Information Theory
Conditional mixture model for correlated neuronal spikes
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
Discovering the visual patterns elicited by human scan-path
Proceedings of the 2010 conference on Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society
Applying the multivariate time-rescaling theorem to neural population models
Neural Computation
The Ising decoder: reading out the activity of large neural ensembles
Journal of Computational Neuroscience
CorLayer: a transparent link correlation layer for energy efficient broadcast
Proceedings of the 19th annual international conference on Mobile computing & networking
Discovering the multi-neuronal firing patterns based on a new binless spike trains measure
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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Spike trains recorded from populations of neurons can exhibit substantial pairwise correlations between neurons and rich temporal structure. Thus, for the realistic simulation and analysis of neural systems, it is essential to have efficient methods for generating artificial spike trains with specified correlation structure. Here we show how correlated binary spike trains can be simulated by means of a latent multivariate gaussian model. Sampling from the model is computationally very efficient and, in particular, feasible even for large populations of neurons. The entropy of the model is close to the theoretical maximum for a wide range of parameters. In addition, this framework naturally extends to correlations over time and offers an elegant way to model correlated neural spike counts with arbitrary marginal distributions.