rebound spike

The basics of how to develop, test, and use models.
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reza_rzm
Posts: 34
Joined: Tue Aug 23, 2005 10:07 am

rebound spike

Post by reza_rzm »

Dear Freinds,

i create a soma that include passive channel+HH channel and CaT channel.

for CaT channel i used CaT.mod in toturial,partD;

by injection a negative current i see ' rebound spikes'; but the rebound spike start very fast, after offset of injection current!

what should i do, to get a rebound spike that be similar to experimental data?

ted
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Creating, debugging, and tuning a model

Post by ted »

i create a soma that include passive channel+HH channel and CaT channel.
What do you mean by "HH channel? There is no such thing as an "HH
channel." There is an "HH model of the action potential," which consists
of three ionic currents: voltage-gated sodium and potassium currents, and
a leak current. So what did you really put into the soma?

If you inserted NEURON's hh mechanism into the soma, you inserted all
three of these ionic currents. Then if you also insert pas, your soma has
two voltage-gated currents, and two leak currents: il_hh and i_pas. Do
you really want to have two leak currents?
what should i do, to get a rebound spike that be similar to experimental data?
The first step in building a computational model is to start with a clear
conceptual model. This conceptual model is a hypothesis in which you
propose a minimal list of anatomical and biophysical properties that,
in your best judgment, are responsible for a particular phenomenon.
To the extent possible, you try to base the anatomical and biophysical
properties on experimental observations, and where such observations
are incomplete, you supplement them with "reasonable" (and, we hope,
verifiable) guesses.

Once you have a clear conceptual model, it is time to build a
computational model, while trying to ensure that what is in the
computer (the computational model) is a close match to what is
in your head (the conceptual model).

At some point you also have to decide whether you are interested in
qualitative or quantitative results. Which of these targets to aim for
depends on your motivation for modeling. For example, if you only
want to know if a particular potassium current might account for
accommodation or burst firing, then a qualitative result is sufficient.

In most cases a qualitative result is most appropriate. The first try
never produces results that are a close quantitative match to the
experimental observations. Count yourself lucky if you see
something that is qualitatively remotely similar to experimental
results.

If a computational model produces unexpected results, the first thing
to do is check your work. Did you make a mistake in implementing
the model? Are the properties (channel densities, kinetics) of the
individual components of the model correct? Is it enough to adjust
some model parameters (reversal potentials, channel densities, rate
constants, voltage-dependencies)? Or is there a deeper problem, a
problem with the conceptual model itself? Is the anatomy correct?
Did you choose the right voltage- and/or ligand-gated currents,
buffers, pumps etc.?

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