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
--
Dr. Hans Ekkehard Plesser
Associate Professor
Dept. of Mathematical Sciences and Technology
Norwegian University of Life Sciences
Phone +47 6496 5467
Fax +47 6496 5401
Email [email protected]
Home http://arken.umb.no/~plesser
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