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Many papers published seem to focused on oscillations to characterize synchronisation and communication between different brain areas. For example, this paper from Voytek et. al where communication between brain areas increases with the abstractedness of the task.
Are there models that relate neuron firing to these types of oscillatory readings? In other words, given spiking data and location of a neuron ensemble, would it be possible to model the types of oscillations that might be read?
The oscillations measured by EEG, ECoG, and MEG are thought to originate in the apical dendrites' fluctuating potentials. Try looking into the forward problem of EEG. Like the inverse problem but in reverse. Given a known source potential (apparently it's very common to model cortical neurons' LFP as a distribution of current dipoles?) you solve the Poisson equation to get the potential at the recording electrode. I'm reading through this review right now and it's very interesting: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2234413/.
Other avenues which seem interesting are neural mass/neural field models, especially Walter Freeman's work on neurodynamics. See: http://www.scholarpedia.org/article/Freeman_K-set.
Abstracted away from the biology a bit there's Jack Cowan's field models and stochastic neural networks. But they're both absolutely prolific and the math is dense and I'm not quite sure where to even really begin.
You really want to read the work by Gyorgy Buzsaki. The recent Schomburg et al., 2014 is quite illuminating although quite dense. Try my F1000 review for starters.
http://f1000.com/prime/contributor/evaluate/article/718892254
The original paper can be found here
http://www.sciencedirect.com/science/article/pii/S0896627314007818
