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Development of statistical tests and tools for comparison of neural activity data with the aim of validating simulations.
The marginal of the collection of all CCHs where the time lag dimension is reduced can be represented as a generalized correlation matrix.
The matrix entries could be:
Clustering of CC matrix
Distance Measure
Extending the view on correlation via the Pearson correlation coefficient (which only regards bin-wise synchrony) the correlation should be analyzable with both
Should the function be collected separately or should the functionality be included in the non-time-resolved function via a parameter?
For a network of N neurons, there are N*(N-1)/2 CCHs. The resulting data is 4 dimensional (Neuron#1, Neuron#2, Time-Lag, Correlation). Significant features in this data have to extracted, visualized and quantified.
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