Command: tsodyks2_synapse


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.


The following parameters can be set in the status dictionary:
U double - probability of release increment (U1) [0 1] default=0.5
u double - Maximum probability of release (U_se) [0 1] default=0.5
x double - current scaling factor of the weight default=U
tau_rec double - time constant for depression in ms default=800 ms
tau_fac double - time constant for facilitation in ms default=0 (off)

Marc-Oliver Gewaltig based on tsodyks_synapse by Moritz Helias


[1] Tsodyks M. V. & Markram H. (1997). The neural code between neocortical pyramidal neurons
depends on neurotransmitter release probability. PNAS 94(2) 719-23.
[2] Fuhrmann G. Segev I. Markram H. & Tsodyks M. V. (2002). Coding of temporal
information by activity-dependent synapses. Journal of neurophysiology 87(1) 140-8.
[3] Maass W. & Markram H. (2002). Synapses as dynamic memory buffers. Neural networks 15(2)


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