Dear Hans,
Further to Alan's email, I thought I'd refer you to some models we have
in the CellML Model Repository. Have a look at some of Dr Richard
Bertram's models at http://www.cellml.org/models - most of these are
models of bursting in pancreatic beta cells, but there are a couple of
neuronal models there too. You can find them under 'Bertram' in the
repository listing.
If your simple conditional statements can be covered by piecewise
equations, it can be done in CellML.
You might do well to look at some of the electrophysiological models of
heart cells that we've coded up -
http://www.cellml.org/models/beeler_reuter_1977_version08 is a fairly
simple model with a piecewise-based stimulus protocol added.
Please let me know if you need any help accessing the models in the
repository.
Best of luck!
Kind regards,
James Lawson
Dr. Hans Ekkehard Plesser wrote:
Hi!
We are looking for a good way to describe and share neuron models, and CellML
appears a good
candidate. The neuron models we are interested in consist mostly of a single
compartment,
possibly of a small number of compartments.
As far as I can see, CellML appears well suited to describe the so-called
subthreshold dynamics
of model neurons. But I am wondering if CellML can also capture (or be extended
to capture) the
threshold operation present in most simplified neuron models. Briefly, the
model dynamics are
integrated according to a set of differential equations. When the membrane
potential of the
neuron crosses a certain threshold, the neuron is said to "fire": the membrane
potential is
reset to a certain value instantaneuously, and often clamped at that value for
a certain period
of time afterwards (refractory period); also, an output signal is generated. In
simple
pseudocode, this would look like:
while ( simulation time not up )
process input
update dynamics according to ODE
if ( neuron is refractory )
V_m = V_reset
count down "refractoriness"
if ( V_m > Threshold )
V_m = V_reset
emit output signal
count up time
Best regards,
Hans
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