| 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.
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| Parameters: |
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_rec double - time constant for facilitation in ms, default=0 (off)
Notes:
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.
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| References: |
[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), 155–61.
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