Command: tsodyks_synapse



[1] Tsodyks Uziel Markram (2000) Synchrony Generation in Recurrent Networks
with Frequency-Dependent Synapses. Journal of Neuroscience vol 20 RC50


This synapse model implements synaptic short-term depression and short-term facilitation
according to [1]. In particular it solves Eqs (3) and (4) from this paper in an
exact manner.

Synaptic depression is motivated by depletion of vesicles in the readily releasable pool
of synaptic vesicles (variable x in equation (3)). Synaptic facilitation comes about by
a presynaptic increase of release probability which is modeled by variable U in Eq (4).
The original interpretation of variable y is the amount of glutamate concentration in
the synaptic cleft. In [1] this variable is taken to be directly proportional to the
synaptic current caused in the postsynaptic neuron (with the synaptic weight w as a
proportionality constant). In order to reproduce the results of [1] and to use this
model of synaptic plasticity in its original sense the user therefore has to ensure
the following conditions:

1.) The postsynaptic neuron must be of type iaf_psc_exp or iaf_tum_2000 because
these neuron models have a postsynaptic current which decays exponentially.

2.) The time constant of each tsodyks_synapse targeting a particular neuron
must be chosen equal to that neuron's synaptic time constant. In particular that means
that all synapses targeting a particular neuron have the same parameter tau_psc.

However there are no technical restrictions using this model of synaptic plasticity
also in conjunction with neuron models that have a different dynamics for their synaptic
current or conductance. The effective synaptic weight which will be transmitted
to the postsynaptic neuron upon occurrence of a spike at time t is u(t)*x(t)*w where
u(t) and x(t) are defined in Eq (3) and (4) w is the synaptic weight specified upon
The interpretation is as follows: The quantity u(t)*x(t) is the release probability
times the amount of releasable synaptic vesicles at time t of the presynaptic neuron's
spike so this equals the amount of transmitter expelled into the synaptic cleft.
The amount of transmitter than relaxes back to 0 with time constant tau_psc of the
synapse's variable y.
Since the dynamics of y(t) is linear the postsynaptic neuron can reconstruct from the
amplitude of the synaptic impulse u(t)*x(t)*w the full shape of y(t).
The postsynaptic neuron however might choose to have a synaptic current that is not
necessarily identical to the concentration of transmitter y(t) in the synaptic cleft.
It may realize an arbitrary postsynaptic effect depending on y(t).


The following parameters can be set in the status dictionary:
U double - maximum probability of release [0 1]
tau_psc double - time constant of synaptic current in ms
tau_fac double - time constant for facilitation in ms
tau_rec double - time constant for depression in ms
x double - initial fraction of synaptic vesicles in the readily releasable pool [0 1]
y double - initial fraction of synaptic vesicles in the synaptic cleft [0 1]

Moritz Helias