tsodyks2_synapse – Synapse type with short term plasticity

Description

This synapse model implements synaptic short-term depression and short-term facilitation according to 1 and 2. It solves Eq (2) from 1 and modulates U according to eq. (2) of 2.

This connection merely scales the synaptic weight, based on the spike history and the parameters of the kinetic model. Thus, it is suitable for all types of synaptic dynamics, that is current or conductance based.

The parameter A_se from the publications is represented by the synaptic weight. The variable x in the synapse properties is the factor that scales the synaptic weight.

Parameters

The following parameters can be set in the status dictionary:

U

real

Parameter determining the increase in u with each spike (U1) [0,1], default=0.5

u

real

The probability of release (U_se) [0,1], default=0.5

x

real

Current scaling factor of the weight, default=U

tau_fac

ms

Time constant for facilitation, default = 0(off)

tau_rec

ms

Time constant for depression, default = 800ms

Remarks:

Under identical conditions, the tsodyks2_synapse produces slightly lower peak amplitudes than the tsodyks_synapse. However, the qualitative behavior is identical. The script test_tsodyks2_synapse.py in the examples compares the two synapse models.

References

1(1,2)

Tsodyks MV, Markram H (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. PNAS, 94(2):719-23. DOI: https://doi.org/10.1073/pnas.94.2.719

2(1,2)

Fuhrman, G, Segev I, Markram H, Tsodyks MV (2002). Coding of temporal information by activity-dependent synapses. Journal of Neurophysiology, 87(1):140-8. DOI: https://doi.org/10.1152/jn.00258.2001

3

Maass W, Markram H (2002). Synapses as dynamic memory buffers. Neural Networks, 15(2):155-61. DOI: https://doi.org/10.1016/S0893-6080(01)00144-7

Transmits

SpikeEvent

See also

Synapse, Short-Term Plasticity