L5 Pyramidal Neuron - Almog and Korngreen 2014

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.

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.

NeuroConstruct project

Ionic 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.


Cell morphology from the modelDB project was exported to NeuroML using neuron.

Idiosyncrasies in the conversion

General issues

General comments/difficulties that occurred during the conversion are logged on the github repo as issues.

Inhomogeneous parameters over the dendritic tree

The original neuron version results in piecewise linear interpolation of the distribution over each section. Compare the neuroConstuct generated code

 //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))