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==== EEG / MEG / LFP ====
EEG and MEG data support the estimation of more biologically detailed neural models than fMRI, as their higher temporal resolution provide access to richer neural dynamics. The models can be classed into phenomenological models, which focus on reproducing particular data features, and physiological models, which recapitulate neural circuity. The physiological models can be further subdivided into two classes - convolution models, which convolve pre-synaptic input by a synaptic kernel function, and [http://www.scholarpedia.org/article/Conductance-based_models conductance-based models], which derive from the equivalent circuit representation of the cell membrane developed by Hodgkin and Huxley<ref>{{Cite journal|last=Hodgkin|first=A. L.|last2=Huxley|first2=A. F.|date=1952-04-28|title=The components of membrane conductance in the giant axon ofLoligo|url=http://dx.doi.org/10.1113/jphysiol.1952.sp004718|journal=The Journal of Physiology|volume=116|issue=4|pages=473–496|doi=10.1113/jphysiol.1952.sp004718|issn=0022-3751}}</ref>:
EEG and MEG data can support the estimation of more biologically detailed neural models than fMRI, as their higher temporal resolution provide access to richer neural dynamics. The predominant model is DCM for evoked responses (DCM for ERP)<ref>{{Cite journal|last=David|first=Olivier|last2=Friston|first2=Karl J.|date=2003-11|title=A neural mass model for MEG/EEG:|url=http://dx.doi.org/10.1016/j.neuroimage.2003.07.015|journal=NeuroImage|volume=20|issue=3|pages=1743–1755|doi=10.1016/j.neuroimage.2003.07.015|issn=1053-8119}}</ref><ref>{{Citation|last=Kiebel|first=Stefan J.|title=Dynamic Causal Modeling for Evoked Responses|date=2009-07-31|url=http://dx.doi.org/10.7551/mitpress/9780262013086.003.0006|work=Brain Signal Analysis|pages=141–170|publisher=The MIT Press|isbn=9780262013086|last2=Garrido|first2=Marta I.|last3=Friston|first3=Karl J.}}</ref>. It is a biologically plausible neural mass model, building on the work of several earlier authors especially Jansen and Rit <ref>{{Cite journal|last=Jansen|first=Ben H.|last2=Rit|first2=Vincent G.|date=1995-09-01|title=Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns|url=http://dx.doi.org/10.1007/s004220050191|journal=Biological Cybernetics|volume=73|issue=4|pages=357–366|doi=10.1007/s004220050191|issn=0340-1200}}</ref>. It emulates the activity of a cortical area using three neuronal sub-populations, each of which rests on two operators. The first transforms the pre-synaptic firing rate into a Post-Synaptic Potential (PSP), by [[Convolution|convolving]] a synaptic response function (kernel) by the pre-synaptic input. As a result, this is referred to as a [[convolution]] model. The second operator, a [[Sigmoid function|sigmoid]] function, transforms the membrane potential into a firing rate of action potentials. A subsequent extension to this model, DCM for LFP (Local Field Potentials), added the effects of specific ion channels on spike generation <ref>{{Cite journal|last=Moran|first=R.J.|last2=Kiebel|first2=S.J.|last3=Stephan|first3=K.E.|last4=Reilly|first4=R.B.|last5=Daunizeau|first5=J.|last6=Friston|first6=K.J.|date=2007-09|title=A neural mass model of spectral responses in electrophysiology|url=http://dx.doi.org/10.1016/j.neuroimage.2007.05.032|journal=NeuroImage|volume=37|issue=3|pages=706–720|doi=10.1016/j.neuroimage.2007.05.032|issn=1053-8119}}</ref>. ▼
* Physiological models:
** Convolution models:
▲
*** DCM for LFP (Local Field Potentials) <ref>{{Cite journal|last=Moran|first=R.J.|last2=Kiebel|first2=S.J.|last3=Stephan|first3=K.E.|last4=Reilly|first4=R.B.|last5=Daunizeau|first5=J.|last6=Friston|first6=K.J.|date=2007-09|title=A neural mass model of spectral responses in electrophysiology|url=http://dx.doi.org/10.1016/j.neuroimage.2007.05.032|journal=NeuroImage|volume=37|issue=3|pages=706–720|doi=10.1016/j.neuroimage.2007.05.032|issn=1053-8119}}</ref>. Extends DCM for ERP by added the effects of specific ion channels on spike generation.
*** Canonical Microcircuit (CMC) <ref>{{Cite journal|last=Bastos|first=Andre M.|last2=Usrey|first2=W. Martin|last3=Adams|first3=Rick A.|last4=Mangun|first4=George R.|last5=Fries|first5=Pascal|last6=Friston|first6=Karl J.|date=2012-11|title=Canonical Microcircuits for Predictive Coding|url=http://dx.doi.org/10.1016/j.neuron.2012.10.038|journal=Neuron|volume=76|issue=4|pages=695–711|doi=10.1016/j.neuron.2012.10.038|issn=0896-6273}}</ref>. Introduced to address hypotheses about ascending and descending signals in the brain, which are thought to underpin [[predictive coding]], the which split the pyramidal cell population into deep and superficial populations.
** Conductance models:
***
* Phenomonological
== Model estimation ==
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