[NEST Users] All-to-all connection and gating variable

Dear friends, Thanks to all your help, I've successfully reproduced the behavior of the modified Izhikevich neuron described in the paper: Chen, L., Campbell, S.A. Exact mean-field models for spiking neural networks with adaptation. J Comput Neurosci 50, 445–469 (2022). https://doi.org/10.1007/s10827-022-00825-9 But when I try to connect a population of neurons among themselves, I have problems. In particular, imagine I create 10 neurons with the following Python code: neurons = nest.Create("izhikevich_ODE", 10) Then, I found in some of the examples that the neurons do not come connected, and to connect them all-to-all, I had to do the following: nest.Connect(neurons, neurons, 'all_to_all') Is that right? Then, in the NESTML model, how do I receive the spikes into the gating variable? If I am not wrong, I have to set the input as input: spikes real <- spike and then add them to the synaptic gating variable as a simple convolution: kernel G = delta(t) SS' = ((-SS/tau) + S_jump/N * convolve(G, spikes)) /s Note: the /s is because the equations in the paper are without units. Is this right? As kernel, I also tried exp(-t/tau), with the same tau as the one used for SS, but then it seemed to take forever to compute, so I left the delta... However, if the spikes themselves are Dirac deltas, I do not completely understand the meaning of this convolution... What is the correct way to get the spikes from the other neurons? thanks! gus.-