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
photo
*Florian Fiebig, PhD*
Researcher
Computational Brain Science
+46 70-744-7439 <tel:+16505426046> | Skype: florianfiebig <#>
<http://www.numenta.com/>
<http://us.linkedin.com/in/florian-fiebig-b387b886> Most recent
papers:<https://rdcu.be/bRLmu>
<https://doi.org/10.1523/JNEUROSCI.1989-16.2016>
<https://rdcu.be/bRLmu>
<https://www.eneuro.org/content/early/2020/02/28/ENEURO.0374-19.2020>
On 2/15/2022 11:49 AM, Alexander Kozlov wrote:
Hello,
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,
CST EECS KTH.
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