L5 Pyramidal Neuron Almog and Korngreen (2014)¶

Model description¶

This model originally appeared in *Almog M, Korngreen A * A Quantitative Description of Dendritic Conductances and Its Application to Dendritic Excitation in Layer 5 Pyramidal Neurons J Neurosci 34(1):1.
The relevant *neuron files can be downloaded from modelDB.
h2. Conversion to NeuroML
We have converted the original model to NeuroML. Most .mod mechanisms can be mapped to ChannelML with exception of the Calcium mechanisms described in terms of the GHK formalism. These latter needed to be translated to NeuroML2, which supports arbitrary current laws.
The NeuroML version reproduces the results from the bac6.ses script bundled with the modelDB version, taking into account that some issues .

h3. NeuroConstruct project
h4. Ion Mechanisms
Both original and NeuroML versions of the ionic mechanisms were imported into a neuroConstruct project, with simulation configurations allowing the comparison of both implementations. Mechanisms were named according the following convention: the original .mod_ mechanisms were suffixed with *nrn, while the NeuroML mechanisms had no suffix.

h4. Morphology
Cell morphology from the*modelDB_ project was exported to NeuroML using neuron.
h3. Idiosyncrasies in the conversion
h4. General issues
General comments/difficulties that occurred during the conversion are logged on the github repo as issues .
h4. Inhomogeneous parameters over the dendritic tree
p=. []({width: 80%}gih_comparison.png)
The original neuron version results in piecewise linear interpolation of the distribution over each section.
Compare the neuroConstuct generated code
<pre>
//neuroconstruct generated
objref PathLengthApicalDends
PathLengthApicalDends = new SubsetDomainIterator
PathLengthApicalDends.update {
x = PathLengthApicalDends.x
p = PathLengthApicalDends.p
gmax_iA = 100000.0** (2.82824E-4 + (0.0033165 * exp(((–1) * 0.0117721) * p))) // 100000.0 to convert from nc to NEURON units
}

With the original .hoc specification

gbar_iA(0:1) = (gka_start+gka_beta*exp(gka_alpha*dist1)):(gka_start+gka_beta*exp(gka_alpha*dist2))