The effect of correlated variability on the accuracy of a population code
Neural Computation
Neural Assemblies, an Alternative Approach to Artificial Intelligence
Neural Assemblies, an Alternative Approach to Artificial Intelligence
Synchronous firing and higher-order interactions in neuron pool
Neural Computation
Information-geometric measure for neural spikes
Neural Computation
Spatially organized spike correlation in cat visual cortex
Neurocomputing
Generation of Synthetic Spike Trains with Defined Pairwise Correlations
Neural Computation
How precise is neuronal synchronization?
Neural Computation
Can spike coordination be differentiated from rate covariation?
Neural Computation
Correlations and population dynamics in cortical networks
Neural Computation
Generation of correlated spike trains
Neural Computation
Generating spike trains with specified correlation coefficients
Neural Computation
State-space analysis on time-varying correlations in parallel spike sequences
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Efficient identification of assembly neurons within massively parallel spike trains
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Efficient identification of assembly neurons within massively parallel spike trains
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Distinguishing the causes of firing with the membrane potential slope
Neural Computation
A new method to infer higher-order spike correlations from membrane potentials
Journal of Computational Neuroscience
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Recent developments in electrophysiological and optical recording techniques enable the simultaneous observation of large numbers of neurons. A meaningful interpretation of the resulting multivariate data, however, presents a serious challenge. In particular, the estimation of higher-order correlations that characterize the cooperative dynamics of groups of neurons is impeded by the combinatorial explosion of the parameter space. The resulting requirements with respect to sample size and recording time has rendered the detection of coordinated neuronal groups exceedingly difficult. Here we describe a novel approach to infer higher-order correlations in massively parallel spike trains that is less susceptible to these problems. Based on the superimposed activity of all recorded neurons, the cumulant-based inference of higher-order correlations (CuBIC) presented here exploits the fact that the absence of higher-order correlations imposes also strong constraints on correlations of lower order. Thus, estimates of only few lower-order cumulants suffice to infer higher-order correlations in the population. As a consequence, CuBIC is much better compatible with the constraints of in vivo recordings than previous approaches, which is shown by a systematic analysis of its parameter dependence.