Yuriy Mishchenko Papers:
In this paper we develop a statistical method for inferring neural connectivity matrix from neural population spontaneous activity recording with fluorescent calcium imaging, and study how well such performance may be done under various experimental conditions. We find that such inference may be performed remarkably well, allowing quite accurate estimation of the functional connectivity matrix under plausible neuro-biological and imaging conditions from observation containing ~1500-3000 spikes per neuron (~5-10 min observation of neural population spontaneously spiking ~5Hz). One interesting, unexpected conclusion is that this observation time requirement does not grow with the size of neural population. Ie, even for very large neural circuits, ~100-1000 neurons, connectivity matrix can be inferred by monitoring their activity for the same duration. That's definitely a very valuable property of this method.
The package containing Matlab code used in this work can be downloaded from here NETFIT_package.zip. Full text
Mishchenko Y., Vogelstein J., Paninski L. (2011) "A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data.", Annals of Applied Statistics, 5, 1229