Command: tsodyks_synapse

Transmits

SpikeEvent

[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

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).

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]

References

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

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

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

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]

Author

Moritz Helias

File

models/tsodyks_connection.h

FirstVersion

March 2006