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    <p>@deprecated's comments are the exact answer to the question, when it comes to the pure-code python solution. The comments were very valuable, but I feel like I should add some notes for people searching for an answer in the specific context of neural networks.</p> <p>When you take the average membrane potential of large assemblies of neurons, like I did, the correlation will be relatively weak. What you want to look at, primarily, is either the correlation between spike trains, the latency or the excitability (i.e. synaptic efficacy) of the individual assemblies. This can be found relatively easily by just looking at points where the potential exceeds a certain threshold. Scipy's correlation function on spike trains will show a much more detailed picture of interdependence between neurons or neural assemblies when you give it spike trains, as opposed to the actual potentials. You can also take a look at Brian's statistics module, which can be found here:</p> <p><a href="http://neuralensemble.org/trac/brian/browser/trunk/brian/tools/statistics.py" rel="nofollow">http://neuralensemble.org/trac/brian/browser/trunk/brian/tools/statistics.py</a></p> <p>As for phase difference, it probably is an inadequate measure, because neurons are not harmonic oscillators. If you want to take very precise measurements of phase, it's best to look at the synchronization of inharmonic oscillators. The mathematical model that describes these kinds of oscillators, which is very useful in the context of neurons and neural networks, is the Kuramoto model. There is extensive documentation available for the Kuramoto model and Integrate-and-fire-synchronization, so I'll leave it to that.</p>
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