Hej Alex!

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.

Kind regards

Florian Fiebig, PhD
Computational Brain Science
+46 70-744-7439 | Skype: florianfiebig
Most recent papers:
On 2/15/2022 11:49 AM, Alexander Kozlov wrote:

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,
Alexander Kozlov,
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