You are absolutely correct; NESTML is not aware of these parameters as they are
"hard-coded" into the Archiving_Node. We are currently working on eliminating
the Archiving_Node entirely, so that these parameters may be specified by a NESTML synapse
and NESTML neuron model.
In the mean time though, you should still be able to access them via GetStatus() and
SetStatus(). It looks like there is one crucial line missing from the NESTML neuron
template: if you go to your NESTML installation directory, open the file
pynestml/codegeneration/resources_nest/NeuronHeader.jinja2, and insert the following on
Archiving_Node::get_status( __d );
Then, setting and getting Archiving_Node parameters should work as expected:
>> n =
>> nest.SetStatus(n, "tau_minus_triplet", 42.)
>> print(nest.GetStatus(n, "tau_minus_triplet"))
I will add this fix to the NESTML GitHub repository in short order.
Thanks for reporting this; please always feel free to post on this list about NESTML
On Thu, Mar 5, 2020, at 20:07, Alexey Serenko wrote:
Sorry if this is off-topic for this list, but I am facing a problem with NESTML.
After compiling any of the example neuron models present in the
repository, the model seems not to have parameters inherited from
ArchivingNode (such as, for instance, an STDP parameter tau_minus)
available for GetStatus() or SetStatus(). Oddly enough, the model
probably does possess these parameters, because can be connected
to/from TDP synapses, but just does not expose them to Get/SetStatus.
Could you please advise what I should do to examine the problem?
I am using NEST version 2.20 and NESTML version 3.0.post0.dev5 (pip-installed).
graduate student at Kurchatov Institute, Moscow, Russia
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