Scaled correlation analysis: a better way to compute a cross‐correlogram

@article{Nikolic2012ScaledCA,
  title={Scaled correlation analysis: a better way to compute a cross‐correlogram},
  author={Danko Nikolic and Raul Cristian Muresan and Weijia Feng and Wolf Singer},
  journal={European Journal of Neuroscience},
  year={2012},
  volume={35}
}
When computing a cross‐correlation histogram, slower signal components can hinder the detection of faster components, which are often in the research focus. For example, precise neuronal synchronization often co‐occurs with slow co‐variation in neuronal rate responses. Here we present a method – dubbed scaled correlation analysis – that enables the isolation of the cross‐correlation histogram of fast signal components. The method computes correlations only on small temporal scales (i.e. on… 
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References

SHOWING 1-10 OF 184 REFERENCES

Spatiotemporal Structure in Large Neuronal Networks Detected from Cross-Correlation

TLDR
A method based on the positions (phase offsets) of the central peaks obtained from pairwise cross-correlation histograms that reduces data complexity to a one-dimensional representation and helps the investigation of dynamical changes in the preferred firing times of the units.

Detection and assessment of near-zero delays in neuronal spiking activity

A method for the quantification of synchrony and oscillatory properties of neuronal activity

  • P. König
  • Biology
    Journal of Neuroscience Methods
  • 1994

The oscillation score: an efficient method for estimating oscillation strength in neuronal activity.

TLDR
A method that estimates the strength of neuronal oscillations at the cellular level, relying on autocorrelation histograms computed on spike trains, and provides a measure, termed confidence score, that determines the stability of the oscillation score estimate over trials.

Non-parametric detection of temporal order across pairwise measurements of time delays

  • D. Nikolic
  • Computer Science
    Journal of Computational Neuroscience
  • 2006
TLDR
A non-parametric statistical test is proposed with which one can investigate the consistency of the delays across a large number of pairwise measurements and the consistency in the changes in the time delays as a function of experimental conditions.

Correlations, feature‐binding and population coding in primary visual cortex

TLDR
It is suggested that firing rates, rather than correlations, are the main element of the population code for feature binding in primary visual cortex.

Coherence estimation between EEG signals using multiple window time-frequency analysis compared to Gaussian kernels

TLDR
Different approaches to estimate time local coherence between two real valued signals are investigated and results indicate that the method using two dimensional Gaussian kernels has a slightly better average SNR compared to the multiple window approach.

Cross-correlation study of the temporal interactions between areas V1 and V2 of the macaque monkey.

TLDR
Neuroscientists have found that neurons belonging to different cortical areas in the monkey tend to synchronize the time of emission of their action potentials with three different levels of temporal precision.

Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties

TLDR
It is demonstrated here that neurons in spatially separate columns can synchronize their oscillatory responses, which has, on average, no phase difference, depends on the spatial separation and the orientation preference of the cells and is influenced by global stimulus properties.

Can Spike Coordination Be Differentiated from Rate Covariation?

TLDR
A decomposition for the cross-correlation function of doubly stochastic point processes is provided, where each of the components corresponds precisely to the concepts of dependence under investigation, which implies that spike coordination and rate covariation are statistically separable concept of dependence.
...