**Name:**

tsodyks_synapse_hom - Synapse type with short term plasticity using

homogeneous parameters, i.e. all synapses have

the same parameters.

**Description:**

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 connection.

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).

**Parameters:**

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]

**Transmits:**

SpikeEvent

**Remarks:**

The weight and the parameters U, tau_psc, tau_fac, and tau_rec are common to

all synapses of the model and must be set using SetDefaults on the synapse

model.

**References:**

[1] Tsodyks, Uziel, Markram (2000) Synchrony Generation in Recurrent Networks

with Frequency-Dependent Synapses. Journal of Neuroscience, vol 20 RC50

**Author:**

Susanne Kunkel, Moritz Helias

**FirstVersion:**

March 2006

**SeeAlso:**

**Source:**

/home/graber/work-nest/nest-git/nest-simulator/models/tsodyks_connection_hom.h