10 Jun
2020
10 Jun
'20
3:43 p.m.
Dear all,
I am trying to implement a network consisting of LIF neurons with mixed
current based and conductance based synaptic connections.
Eg. one LIF neuron which gets two inputs from which one is treated as
current input and the other as conductance input.
That means, how to treat the input should be encoded in the synapse and
not in the type of the postsynaptic neuron.
Is such a neuron model already implemented or is there a way to
implement this?
Thanks for your help.
Best,
Hartmut
15 Jun
15 Jun
1:29 p.m.
Dear Hartmut,
This should be quite straightforward if you are using NESTML to write your neuron model.
Check out the documentation here for making multiple input ports:
https://nestml.readthedocs.io/en/latest/nestml_language.html#multiple-input…
Your code would look something like this:
input:
spikes_curr pA <- spike # spike input port for current-based synapse
spikes_cond nS <- spike # spike input port for conductance-based synapse
end
equations:
shape syn_kernel = exp(-t / tau_syn)
function I_syn_curr pA = convolve(syn_kernel, spikes_curr)
function I_syn_cond pA = convolve(syn_kernel, spikes_cond) * (V_abs - E_L - E_syn)
V_abs' = -V_abs/tau_m + (I_syn_curr + I_syn_cond) / C_m
end
Please let me know if this works for you.
With kind regards,
Charl Linssen
On Wed, Jun 10, 2020, at 15:43, Hartmut Schmidt wrote:
Dear all,
I am trying to implement a network consisting of LIF neurons with mixed
current based and conductance based synaptic connections.
Eg. one LIF neuron which gets two inputs from which one is treated as
current input and the other as conductance input.
That means, how to treat the input should be encoded in the synapse and
not in the type of the postsynaptic neuron.
Is such a neuron model already implemented or is there a way to
implement this?
Thanks for your help.
Best,
Hartmut
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18 Jun
18 Jun
12:18 p.m.
Dear Charl,
thanks for your help!
It really looks straightforward and I started to implement it using the existing model of
the iaf_cond_exp.nestml, adapting it with your code.
It works very well!
Initially, I had some problems to run nestml. I got an error by just running the model
code without any adaptions using the keyword "shape".
``` errormessage
/pynesml/codegeneration/nest_codegenerator.py line 362, in solve_ode_with_shapes
return analysis(odes_shapes_json, enable_stiffness_check=False)
TypeError: analysis() got an unexpected keyword argument 'enable_stiffness_check'
```
Looking into the code of odetoolbox where analysis is imported from, the function expects
the argument `disable_stiffness_check`.
As suggested in https://github.com/nest/nestml/issues/553, running the code with an older
version of odetoolbox (version 1.0) fixed the problem for me.
Best regards,
Hartmut
1647
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2 comments
3 participants
participants (3)
-
Charl Linssen -
Hartmut Schmidt -
hschmidt＠kip.uni-heidelberg.de