Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Disambiguating different covariation types
Neural Computation
Correlations without synchrony
Neural Computation
Rate Limitations of Unitary Event Analysis
Neural Computation
Information-geometric measure for neural spikes
Neural Computation
Cooperative and temporally structured information in the visual cortex
Signal Processing - Neuronal coordination in the brain: A signal processing perspective
Can spike coordination be differentiated from rate covariation?
Neural Computation
The InfoPhase Method or How to Read Neurons with Neurons
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Impact of Higher-Order Correlations on Coincidence Distributions of Massively Parallel Data
Dynamic Brain - from Neural Spikes to Behaviors
Efficient identification of assembly neurons within massively parallel spike trains
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Measure of correlation orthogonal to change in firing rate
Neural Computation
Bounds of the ability to destroy precise coincidences by spike dithering
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Synergistic coding by cortical neural ensembles
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains
Journal of Computational Neuroscience
Journal of Computational Neuroscience
Finding ensembles of neurons in spike trains by non-linear mapping and statistical testing
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Assembly detection in continuous neural spike train data
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
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It has been proposed that cortical neurons organize dynamically into functional groups (cell assemblies) by the temporal structure of their joint spiking activity. Here, we describe a novel method to detect conspicuous patterns of coincident joint spike activity among simultaneously recorded single neurons. The statistical significance of these unitary events of coincident joint spike activity is evaluated by the joint-surprise. The method is tested and calibrated on the basis of simulated, stationary spike trains of independently firing neurons, into which coincident joint spike events were inserted under controlled conditions. The sensitivity and specificity of the method are investigated for their dependence on physiological parameters (firing rate, coincidence precision, coincidence pattern complexity) and temporal resolution of the analysis. In the companion article in this issue, we describe an extension of the method, designed to deal with nonstationary firing rates.