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:
Your code would look something like this:
spikes_curr pA <- spike # spike input port for current-based synapse
spikes_cond nS <- spike # spike input port for conductance-based synapse
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
Please let me know if this works for you.
With kind regards,
On Wed, Jun 10, 2020, at 15:43, Hartmut Schmidt wrote:
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
Thanks for your help.
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