Dear Charl,
While trying to obtain the minimal code to reproduce the problem, I found
out the problem disappears when I get rid of the setting of the timestep.
At the beginning of the script, I have (because sometimes I want to have
the timestep smaller):
nest.ResetKernel()
dt = 0.1
nest.SetKernelStatus({"resolution": dt, "print_time": True})
And at the meters"
TCmeter = nest.Create("multimeter",params={"interval": dt})
Although getting rid of this last part at the meters doesn't solve the
problem.
In the documentation, I cannot find a change between nest 2.18 and nest 3.1
concerning the resolution. Did I miss something?
Also, setting the clock back to zero doesn't work anymore"
nest.SetKernelStatus({'time':0.})
and I haven't found how I am supposed to do this in NEST 3.
I hope the question is clear. Thank you so much for all your help.
Best,
Nina
On Mon, Nov 8, 2021 at 6:40 PM Charl Linssen <nest-users(a)turingbirds.com>
wrote:
Dear Nina,
If you could provide us with a small, self-contained script that
reproduces the issue, that would be great to help with debugging.
Cheers,
Charl
On Mon, Nov 8, 2021, at 15:46, Nina Doorn wrote:
Dear all,
I wanted to let you know that I resolved the original problem (with
building my own nest module in tvb-multiscale docker container).
I have created my own docker image of the tvb-multiscale but with NEST 3.1
installed. In this container, I can easily install the example
nest-extension module, exactly like is explained in the tutorial. Thus, the
problem was indeed with the nest version.
Thank you all for your answers and help!
However, now I have a different problem. So I am now working with (new
setup) NEST 3.1 and python 3.9.2 instead of (old setup) NEST 2.18 on Python
3.7.3. I have a simple script to simulate the response of a few unconnected
different aeif_cond_alpha_multisynapse neurons to a transient input
current. If I run this script in the old setup, it works perfectly and runs
in under a second. However, if I run it in the new setup, simulations take
forever (I haven't been able to finish one).
Does anyone know if this could be attributable to a difference between
nest 2 and nest 3 that I haven't incorporated into the script?
Should I provide you with the entire script?
Thanks in advance!
Kind regards,
Nina
On Thu, Nov 4, 2021 at 3:19 PM Nina Doorn <n.doorn(a)student.utwente.nl>
wrote:
Hi Hans,
Thank you for letting me know. NMDA receptor conductivity depends on the
membrane potential of the post-synaptic neuron (because of the receptor
blocking with a magnesium ion). So I would like to multiplicate the NMDA
current by a factor which depends on the post-synaptic membrane potential.
Is this possible to implement in NEST?
Best,
Nina
On Thu, Nov 4, 2021 at 2:38 PM Hans Ekkehard Plesser <
hans.ekkehard.plesser(a)nmbu.no> wrote:
Dear Nina,
New synaptic dynamics can be added to existing neuron models, mostly
independent of the membrane potential dynamics of the model.
Concerning non-linear NMDA synapses, depending on what kind of
non-linearity you want to imlement (just voltage gating or also non-linear
interaction between different synapses onto a given neuron), achieving an
efficient implementation can be challenging.
Best,
Hans Ekkehard
--
Prof. Dr. Hans Ekkehard Plesser
Head, Department of Data Science
Faculty of Science and Technology
Norwegian University of Life Sciences
PO Box 5003, 1432 Aas, Norway
Phone +47 6723 1560
Email hans.ekkehard.plesser(a)nmbu.no
Home
http://arken.nmbu.no/~plesser
On 04/11/2021, 14:11, "Nina Doorn" <n.doorn(a)student.utwente.nl> wrote:
Dear Hans,
Thank you for the quick response. Yes I am trying to install the example
module exactly as cloned from Github, I haven't altered anything. I think
the problem might be indeed as Charl described. But it still could be that
it is also a problem that the config.h file is not in the source directory.
Thank you very much for the information on the other models! That is very
useful! I will definitely take a look at the first model (since parameters
are available for different types of thalamic neurons). However, I want to
model, besides AMPA and GABA receptors, NMDA receptors. I know modelling
actual non-linear NMDA receptors is not possible with the available NEST
models. However, what I have done so far with the
aeif_cond_beta_multisynapse, is to define different receptors with
different time constants corresponding to AMPA, NMDA and GABA post-synaptic
potentials. This would not be possible with the NEST models you mention
above. However, I will definitely take a look at them and re-evaluate the
importance of modelling these different beta-synapse receptors.
Thanks again and have a nice day!
Kind regards,
Nina
On Thu, Nov 4, 2021 at 1:23 PM Hans Ekkehard Plesser <
hans.ekkehard.plesser(a)nmbu.no> wrote:
Dear Nina,
The first error is
In file included from
/home/docker/nest-extension-module-master/src/mymodule.cpp:30:
/home/docker/nest-extension-module-master/src/pif_psc_alpha.h:92:1: error:
expected class-name before ‘{’ token
{
^
and it looks a lot like everything following are consequences of this
error. So if looks as if something may be off in the pif_psc_alpha.h file
around lines 90-92. Are you trying to compile the example module exactly as
cloned from Github or have you made any changes to the code?
There could also be a small chance of problems "spilling" from the
config.h file, which is in the build (not source) directory. That could
explain why you experience problems using the docker container, while all
works for your colleagues using Linux.
BTW, do you know the adaptive multi-timescale models from the Shinomoto
group (amat2_exp_psc), which can reproduce the same 20 response patterns as
the Izhikevich model, but are mathematically simpler as they are linear?
See
.. [3] Kobayashi R, Tsubo Y and Shinomoto S (2009). Made-to-order
spiking neuron model equipped with a multi-timescale adaptive
threshold. Frontiers in Computational Neuroscience, 3:9.
DOI:
https://dx.doi.org/10.3389%2Fneuro.10.009.2009
.. [4] Yamauchi S, Kim H, Shinomoto S (2011). Elemental spiking neuron
model
for reproducing diverse firing patterns and predicting precise
firing times. Frontiers in Computational Neuroscience, 5:42.
DOI:
https://doi.org/10.3389/fncom.2011.00042
We also have the glif model families from the Allen institute available in
NEST (glif_cond, glif_psc), see
.. [1] Teeter C, Iyer R, Menon V, Gouwens N, Feng D, Berg J, Szafer A,
Cain N, Zeng H, Hawrylycz M, Koch C, & Mihalas S (2018)
Generalized leaky integrate-and-fire models classify multiple
neuron
types. Nature Communications 9:709.
These may be more up-to-date alternatives to the Izhikevich model. For
some experiences with that model, see
Pauli R, Weidel P, Kunkel S and Morrison A (2018) Reproducing
Polychronization: A Guide to Maximizing the Reproducibility of Spiking
Network Models. *Front. Neuroinform*. 12:46. doi: 10.3389/fninf.2018.00046
Best regards,
Hans Ekkehard
--
Prof. Dr. Hans Ekkehard Plesser
Head, Department of Data Science
Faculty of Science and Technology
Norwegian University of Life Sciences
PO Box 5003, 1432 Aas, Norway
Phone +47 6723 1560
Email hans.ekkehard.plesser(a)nmbu.no
Home
http://arken.nmbu.no/~plesser
On 04/11/2021, 11:55, "Nina Doorn" <n.doorn(a)student.utwente.nl> wrote:
Dear experts,
To develop a spiking neuronal network model of the thalamus, I want to
adapt the Izhikevich neuron model to account for the behavior of
thalamocortical neurons. Before I do this, I wanted to test if it was
possible to install an extension module in my setup. Therefore I followed
these steps:
https://nest-extension-module.readthedocs.io/en/latest/extension_modules.ht…
to install this example nest-extension-module:
https://github.com/nest/nest-extension-module .
I am working with the tvb-multiscale docker container (
https://github.com/the-virtual-brain/tvb-multiscale/tree/master/tvb_multisc…)
in VScode on windows. I've been working with this succesfully and easily
managed to make thalamus models with the available aeIF neuron model of
NEST. I'm using a python 3.7.3 interpreter and NEST 3.
I've succesfully "made" the module with:
docker@84fabd16af99:~/mmb$ cmake
-Dwith-nest=/home/docker/env/neurosci/nest_build/bin/nest-config
../nest-extension-module-master
It gives me the message:
You can now build and install 'mymodule' using
make
make install
The library file libmymodule.so will be installed to
/home/docker/env/neurosci/nest_build/lib/nest/
Help files will be installed to
/home/docker/env/neurosci/nest_build/share/doc/nest
The module can be loaded into NEST using
nest.Install('mymodule') (in PyNEST)
(mymodule) Install (in SLI
-- Configuring done
-- Generating done
-- Build files have been written to: /home/docker/mmb
However, when I try to "make". I get a bunch of errors that I have added
at the end of this email. My colleague tried to install exactly the same
module in exactly the same way on his linux machine and it worked
perfectly. But somehow for me, I get these weird errors that I haven't
been able to resolve so far. Does anyone have an idea what the problem
might be? It would be greatly appreciated. If you need any additional
information please let me know.
Thank you in advance and have a nice day!
Kind regards,
Nina Doorn
Error message:
docker@84fabd16af99:~/mmb$ make
Scanning dependencies of target mymodule_module
[ 10%] Building CXX object
src/CMakeFiles/mymodule_module.dir/mymodule.cpp.o
In file included from
/home/docker/nest-extension-module-master/src/mymodule.cpp:30:
/home/docker/nest-extension-module-master/src/pif_psc_alpha.h:92:1: error:
expected class-name before ‘{’ token
{
^
/home/docker/nest-extension-module-master/src/pif_psc_alpha.h:115:21:
error: type ‘nest::Node’ is not a base type for type
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