9 May
2020
9 May
'20
1:08 p.m.
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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