Me and Nikolaos Chrysanthidis use and aeif_cond_exp with
Tsodyks-Markram STP all the time with NEST on the supercomputer.
Check any of our recent publications for that. Its a custom
implementation originally done by Phil Tully, a former PhD student
of Anders Lansner. The catch is that it also comes with the BCPNN
learning rule for the Hebbian learning component, but ofcourse you
could switch that off by setting the BCPNN plasticity time
constants( or just the plasticity modulator switch kappa) to zero if
you want static weights modulated by TM-based STP only. The
TM-mechanisms parameters tau_fac, tau_rec, and U are independent of
that, as the TM rule is multiplicative with the underlying weight,
or rather the conductance, as its a conductance based model
ofcourse. Hope this helps.
On 2/15/2022 11:49 AM, Alexander Kozlov
Documentation for `tsodyks_synapse` says it is only compatible with `iaf_psc_exp` or `iaf_psc_exp_htum` neuron models. Would it be possible to use it with other `_exp`-type models with postsynaptic currents or conductances with exponential decay (for example, `aeif_cond_exp`)? If not, what could be a work around?
With best regards,
CST EECS KTH.
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