Model files from the paper:

Zylbertal et al., "Prolonged Intracellular Na+ Dynamics Govern Electrical
Activity in Accessory Olfactory Bulb Mitral Cells", PLOS Biology (2015)
The file model_demo.py reproduces plots from figures 4-6 of the article
by calling the modules model_definition.py and model_run.py.

Questions on how to use this model should be directed to
asaph.zylbertal@mail.huji.ac.il

Synopsis:

Persistent activity has been reported in many brain areas and is
hypothesized to mediate working memory and emotional brain states and
to rely upon network or biophysical feedback. Here we demonstrate a
novel mechanism by which persistent neuronal activity can be generated
without feedback, relying instead on the slow removal of Na+ from
neurons following bursts of activity. This is a realistic
conductance-based model that was constructed using the detailed
morphology of a single typical accessory olfactory bulb (AOB) mitral
cell for which the electrophysiological properties were
characterized.

A novel feature of the model is the incorporation of
compartmental [Na+]i as state variables along with longitudinal ionic
diffusion. Accordingly, [Na+]i not only sets the local Na+ reversal
potential, but also affects localized ionic extrusion mechanisms
(Na+-K+ pumps, Na+-Ca2+ exchangers). The model assumes that active
conductances reside in the apical dendrites and dendritic tufts, as
well as in the soma and axon initial segment, so that [Na+]i increase
in these compartments following firing.  Using this model and
follow-up experiments we found that the exceptionally slow inward
current that follows bursts of activity in AOB mitral cells is
governed by prolonged dynamics of [Na+]i. Specifically, elevated
dendritic [Na+]i reverses the Na+-Ca2+ exchanger activity, thus
modifies the [Ca2+]i set-point. This process, which relies on
ubiquitous membrane mechanisms, is likely to play a role in other
neuronal types in various brain regions.

Example use:

Extract the archive, run nrnivmodl in the channels directory
(linux/unix) or mknrndll (mswin or mac os x) (see
http://senselab.med.yale.edu/ModelDB/NEURON_DwnldGuide.html for more
help) to compile the channels, and run the file model_demo.py. After a
while, It will produce the main figures from the paper that are based
on the model: