Known issues with Traub et al 2005.

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This is a quite complex and detailed model and as discussed in the [original paper](http://www.ncbi.nlm.nih.gov/pubmed/15525801?dopt=Abstract)

> Any model, even of a small bit of cortex, is subject to difficulties and hazards: limited data, large numbers of parameters, criticisms that models with complexity comparable to the modeled system cannot be scientifically useful, the expense and slowness of the necessary computations, and serious uncertainties as to how a complex model can be compared with experiment and shown to be predictive.

> The above difficulties and hazards are too real to be dismissed readily. In our opinion, the only way to proceed is through a state of denial that any of the difficulties need be fatal. The reader must then judge whether the results, preliminary as they must be, help our understanding.

Even the published Fortran version of this model was acknowledged to be incomplete. Each conversion of this model will deviate to a small or large extent from this version.

### Questions about physiological properties of model

**Dependence on Fast Regular Bursting cells for oscillatory behaviour**

**Prevalence of gap junctions**

**High current threshold for deep pyramidal firing**

**Not tested with external synaptic input**

### Limitations of the conversion of the model to NEURON

It is useful to read the [notes on conversion of this model to NEURON from Fortran](http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=82894&file=\nrntraub\README) by Tom Morse and Michael Hines

An updated version of this model in NEURON is being worked on [here](https://github.com/hglabska/Thalamocortical/network)

### Limitations of the conversion of the model to MOOSE

TODO…

### Limitations of the conversion of the model to NeuroML

**Optimal spatial discretisation for each cell needs to be investigated**

Important details of the process of conversion of the cell models to NeuroML, and matching cell behaviour across simulators is present in the [2010 NeuroML paper](http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000815).

The spatial discretisation of the cells influenced precise spike timing. Changing the number of compartments/points used to calculate the membrane potential changed the timing of the cell (e.g. changing the value of nseg in NEURON on all sections). See below for an example of how 3 cells with differing numbers of compartments converged at different rates. A) Nucleus reticularis thalami (nRT) cell; B) Superficial Low Threshold spiking (LTS) cell; C) Layer 6 Non-tufted Regular Spiking pyramidal cell. Traces for NEURON (black) and MOOSE (green) and GENESIS (red).

![](http://www.opensourcebrain.org/attachments/download/114/converge.png)

**NMDA conductance wave form**

The NMDA synapse model used in the network has an unconventional form, with a scaling factor rising lineally between 0 and 5ms, and decaying exponentially. This can probably be approximated by a double exponential synapse (coupled with v & [Mg] dependent blocking mechanism).

**Firing rate vs. injected current of cells**

Many of the cells show unusual F/I curves.

![](/attachments/download/113/ifcurve.png)

**Support in NeuroML**

All model elements from the neuroConstruct generated network can be exported to valid NeuroML v1.8.1.

Model can be exported to [(mostly valid) NeuroML 2](https://github.com/OpenSourceBrain/Thalamocortical/tree/master/neuroConstruct/generatedNeuroML2), but there is not yet an application that can handle such detailed NML2 models (but we’re [working on it](https://github.com/NeuroML/org.neuroml.export/blob/development/src/main/java/org/neuroml/export/neuron/NeuronWriter.java)).