tsodyks2_synapse


Name:
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 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)

Transmits:
SpikeEvent  

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] 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.

Author:
Marc-Oliver Gewaltig, based on tsodyks_synapse by Moritz Helias  
FirstVersion:
October 2011  
SeeAlso: Source:
/home/graber/work-nest/nest-git/nest-simulator/models/tsodyks2_connection.h