Correlation between neural spike trains increases with firing rate.


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  • Thanks for the helpful comments. So, is it correct to say that, if we inject two independent (uncorrelated) Poissonian spike trains into the two neurons, then it does not matter how many spikes are we sampling, the cross-correlation will be near zero? Consequently, neither the input nor the output firing rate does matter. However, if the two Poissonians share correlated spikes, then, because of the non-linearity kicks in, the output cross-correlation will scale not only with the injected correlation, but also with the firing rate.

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  • According to elementary statistics, the estimated correlation coefficient of a population increases with the sample size. When applying this principle to cross-correlograms, we can consider firing rate as sample size, thus, for a cell pair with a fixed effective connectivity higher firing rates will give us higher cross-correlations. Unless something is special about cross-correlograms, I don't see the need for an experimental verification on this. If someone could explain it to me why this is not obvious, I would appreciate.

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