<p>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.<br>
<a href="http://f1000.com/prime/contributor/evaluate/article/718892254" rel="nofollow">http://f1000.com/prime/contributor/evaluate/article/718892254</a></p>

<p>The original paper can be found here<br>
<a href="http://www.sciencedirect.com/science/article/pii/S0896627314007818" rel="nofollow">http://www.sciencedirect.com/science/article/pii/S0896627314007818</a></p>

CC BY-SA 3.0
2016-03-29T16:10:58.157Z
I also plan on posting a working opinion paper on this topic in the near future.
honi
CC BY-SA 3.0
2016-03-29T16:07:45.403Z
2016-03-29T16:07:45.403Z
honi
<p>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: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2234413/" rel="nofollow">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2234413/</a>. </p>

<p>Other avenues which seem interesting are neural mass/neural field models, especially Walter Freeman's work on neurodynamics. See: <a href="http://www.scholarpedia.org/article/Freeman_K-set" rel="nofollow">http://www.scholarpedia.org/article/Freeman_K-set</a>.</p>

<p>Abstracted away from the biology a bit there's Jack Cowan's field models and stochastic neural networks. But they're both absolutely <em>prolific</em> and the math is dense and I'm not quite sure where to even really begin.  </p>

CC BY-SA 3.0
2016-03-29T04:53:44.553Z
2016-03-29T04:53:44.553Z
roguefizixist
<p>Many papers published seem to focused on oscillations to characterize synchronisation and communication between different brain areas. For example, <a href="http://www.nature.com/neuro/journal/v18/n9/full/nn.4071.html" rel="nofollow">this paper from Voytek et. al</a> where <a href="http://voyteklab.com/nature-neuroscience-paper-oscillatory-dynamics-coordinating-human-frontal-networks-in-support-of-goal-maintenance/" rel="nofollow">communication between brain areas increases with the abstractedness of the task</a>.</p>

<p>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?</p>

CC BY-SA 3.0
2016-03-29T00:04:13.467Z
Really interesting question. Look into Dynamical Systems Neuroscience which may give you some  answers
theideasmith
<measurement><theoretical-neuroscience><computational-modeling>
Models relating neuron firing to oscillatory dynamics
Seanny123
# Models relating neuron firing to oscillatory dynamics

(Asked by: username_0 on 2016-03-28)

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](http://www.nature.com/neuro/journal/v18/n9/full/nn.4071.html) where [communication between brain areas increases with the abstractedness of the task](http://voyteklab.com/nature-neuroscience-paper-oscillatory-dynamics-coordinating-human-frontal-networks-in-support-of-goal-maintenance/).

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?

**Tags:** measurement, theoretical-neuroscience, computational-modeling

> **Comments:**
> **username_1**: Really interesting question. Look into Dynamical Systems Neuroscience which may give you some  answers  

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## 2 answers

### Answer by username_2 on 2016-03-29. Score: 1

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>

> **Comments:**
> **username_2**: I also plan on posting a working opinion paper on this topic in the near future.  

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### Answer by username_3 on 2016-03-29. Score: 1

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.


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