11 May
2020
11 May
'20
10:05 a.m.
Dear Charl,
thank you for your reply! I am not familiar with NESTML yet, but I will
go through the documentation you sent and try to implement a model as
you suggested. Using exponential PSC would not be a problem for me.
I had meanwhile found a workaround, though. When setting the parameters
for the vogels_sprekeler_synapse, I set both 'Wmax' and the parameter
'weight' to a negative value:
nest.CopyModel('vogels_sprekeler_synapse',
'plastic_inhibitory',
{'tau': tau_stdp,
'Wmax': -10.,
'eta': eta,
'alpha': alpha,
'weight': -.1,
'weight_recorder': weight_recorder[0]})
It now works, and seems to give me the results as expected. Would you
know if this is equivalent to using the rule with positive weights and
multiplying it by -1 afterwards?
Best,
Júlia
On Sat, 09 May 2020 13:08:20 +0200
"Charl Linssen" <nest-users(a)turingbirds.com> wrote:
Dear Júlia,
Indeed, in some neurons that have only one input port, distinguishing
excitatory from inhibitory spikes is performed on the basis of the
sign of the weight. To avoid this, I would suggest to use a
"multisynapse" type neuron model, that is one that has more than one
input port. You can thus dedicate one port for inhibitory connections
with strictly positive weights (so you can use the
vogels_sprekeler_connection) and move the minus sign to the definition
of I_syn or to the differential equation for V_m.
Unfortunately there is no multisynapse version of the iaf_psc_delta
neuron, but for an example you can look at iaf_psc_exp_multisynapse.
If you need delta-function postsynaptic responses, you'll have to
modify the exp_multisynapse model a little bit. Please let us know if
you run into any trouble.
I'd also like to mention that you can use NESTML to write your neuron
models in, so that you don't have to write any C++. You can find more
information about NESTML on https://github.com/nest/nestml/. Also here
please let us know if you run into any issues!
With kind regards,
Charl
On Wed, May 6, 2020, at 12:49, Julia Gallinaro wrote:
Dear all,
I would like to simulate a network of current based LIF
('iaf_psc_delta') with inhibitory plasticity.
I tried using the model vogels_sprekeler_synapse, but the resulting
weights are positive (and they increase when firing rate is high). I
guess it is because when used with conductance based neurons, the
resulting weights would be multiplied by a gI<0? And with current
based
neurons, this is not the case. The weight is
directly used as the
synaptic weight. Do I understand it right?
Is there a way to implement inhibitory plasticity using the
vogels_sprekeler_synapse and current based LIF?
Best,
Júlia
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