24 May
2023
24 May
'23
4:03 p.m.
Thanks Charl! I successfully added the gap junction to the aeif_cond_alpha
neuron model using NESTML. A very rudimentary quantitative test shows that
without the gap junction, there is a mean Vm difference over the plot of
0.223mV and with a gap junction, this is reduced to 0.043mV. You can see
that there is an initial difference in Vm due to the neurons being
initialized with different values, they converge quickly due to the gap
connection.[image: gap_test_nestml.png]
On Tue, May 16, 2023 at 5:41 PM Charl Linssen <nest-users(a)turingbirds.com>
wrote:
Hi all,
We now have a prototype for support of gap junctions via NESTML.
The code is being tested right now on the "iaf_psc_exp" (linear dynamics)
and "aeif_cond_exp" (nonlinear dynamics) neuron models and can be found at
https://github.com/nest/nestml/pull/907
The pull request is still in a draft stage; some documentation still needs
to be added, but most of all I would like to have more strict numerical
validation of the results. If anyone could recommend some simple systems
(for instance, involving only two neurons connected by one gap junction),
in particular those that have a closed-form algebraic solution of the
dynamics, or those that converge to a known solution under a set of
representative conditions, this would be much appreciated.
Please let me know if you run into any trouble running the prototype!
With kind regards,
Charl
On Tue, Mar 14, 2023, at 11:10, bstrohmer(a)gmail.com wrote:
Hi everyone, has anyone successfully added a gap
junction to an
existing model using NESTML? I would like to add gap junction support
to the aeif_cond_alpha neuron model but it is unclear how this can be
done within NESTML. As suggested by Charl, I started to look into
updating the jinja templates but I am not sure this will be possible
since both the input/output of the neuron and the model's equation must
be updated to support gap junctions. If anyone has any suggestions or
if anyone has NESTML code for adding a gap junction to an existing
model, I would be very appreciative. Thanks!
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