9 Jun
2022
9 Jun
'22
8:29 a.m.
Deaer Pira,
To follow up on Charl's email: Instead of implementing new models in C++, you can use
the NESTML model specification language and then let it create C++ code for you, see
https://nestml.readthedocs.io/en/v5.0.0/ .
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@nmbu.no<mailto:hans.ekkehard.plesser@nmbu.no>
Home http://arken.nmbu.no/~plesser
On 08/06/2022, 18:57, "Charl Linssen"
<nest-users@turingbirds.com<mailto:nest-users@turingbirds.com>> wrote:
Dear Pira,
Thanks for writing in. With this simple two-dimensional linear system, a slow rise and
fast decay is unfortunately not possible. This is a fundamental property of the system. I
guess you probably need a nonlinear system to achieve that (please let me know if you find
any cool ones, I do not currently know any that would exhibit that kind of behaviour).
Perhaps the use of the words "rise" and "decay" in the naming of the
time constants is misleading and should be avoided. In the paper (Rotter & Diesmann
1999) the constants were just named "a" and "b". See for an instance
of a file where both nomenclatures are used
https://github.com/nest/nest-simulator/blob/master/libnestutil/beta_normali….
Note that for the case that a = b, if you are not careful, you will divide by zero. NEST
contains a mechanism to check for that condition, in the file linked above.
Hope this helps,
With kind regards,
Charl
On Wed, Jun 8, 2022, at 13:46, Ravichandran-Schmidt, Pirathitha (SRH Hochschule
Heidelberg) wrote:
Dear NEST Community,
is there a good way to implement a double-exponential function for synapses in C++ within
a nest module?
[cid:image001.jpg@01D87BDB.03361580]
As I need to implement my own neuron model in C++, I adapted the code from extraction the
information from other neuron models (for example: iaf _cond_beta.cpp) and used the
following lines
f[S::DG_EXC] = -y[ S::DG_EXC ] / node.P_.tau_synAMPA_on;
f[S::G_EXC ] = y[ S::DG_EXC ] - y[ S::G_EXC ] / node.P_.tau_synAMPA_off
At first, it seemed to work pretty good. But when I swap the values for rise und decay
time, the evolution of g_ADP did not change at all (for example: rise_time = 1ms,
decay_time = 100ms).
I would be happy to get any feedback on it.
Thanks,
Pira
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