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