Hello Alex and Florian,
The documentation of the Tsodyks synapse explains (at least attempts to explain) some of
the logic behind the interaction between the STP dynamics and the synaptic current
dynamics especially with an eye on using the synapse with other neuron models. If the
documentation should be unclear, please get in touch.
After a brief look at the original paper and the documentation, my impression is the
* If you use any model with psc_exp synaptic dynamics and ensure that tau_psc in the
synapse matches the corresponding tau_syn_? in the neuron model, synaptic dynamics will be
as in the Tsodyks et al paper. Unfortunately, we never found an efficient and
generalizable way to automatically check that the time constants in the synapse model and
the neuron agree, the user has to take care of this.
* If you use the tsodyks synapse with different neuron models, you need to work out
exactly which set of equations applies to your combined synaptic potentiation and synaptic
Looking at it a little more, the situation is as follows:
* In the Tsodyks et al paper, variable y(t) describes the post-synaptic input current
(equation 2) and drives variable z(t) which gives the fraction of inactive states (bottom
of eq 3).
* In NEST, the post-synaptic input current is computed in the neuron model, based on
the synaptic time course for that neuron model. The value y(t) is computed independently
in the tsodyks_synapse for technical reasons.
* To match the Tsodyks model, the current computed in NEST and the value y(t) computed
in the synapse must be identical, which is the case only if the neuron model has
exponential post-synaptic current dynamics with the same time constant as in the
* If you combine the tsodyks_synapse with a neuron model with different synaptic
current dynamics, the STD variables x(t), y(t) and z(t) will describe some dynamics of
short-term depression not directly coupled to the synaptic time course.
Prof. Dr. Hans Ekkehard Plesser
Head, Department of Data Science
Faculty of Science and Technology
Norwegian University of Life Sciences
PO Box 5003, 1432 Aas, Norway
Phone +47 6723 1560
On 16/02/2022, 13:22, "Florian Fiebig"
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.
Florian Fiebig, PhD
Computational Brain Science
+46 70-744-7439<tel:+16505426046> | Skype: florianfiebig
Most recent papers: <https://rdcu.be/bRLmu>
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,
CST EECS KTH.
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