third_factor_stdp_synapse
third_factor_stdp_synapse  Synapse model for spiketiming dependent plasticity with postsynaptic thirdfactor modulation
Description
third_factor_stdp_synapse is a synapse with spike time dependent plasticity (as defined in [1]). Here the weight dependence exponent can be set separately for potentiation and depression. Examples:
Multiplicative STDP [2] mu_plus = mu_minus = 1 Additive STDP [3] mu_plus = mu_minus = 0 Guetig STDP [1] mu_plus, mu_minus in [0, 1] Van Rossum STDP [4] mu_plus = 0 mu_minus = 1
The weight changes are modulated by a “third factor”, in this case the postsynaptic dendritic current I_post_dend
.
I_post_dend
“gates” the weight update, so that if the current is 0, the weight is constant, whereas for a current of 1 pA, the weight change is maximal.
Do not use values of I_post_dend
larger than 1 pA!
References
 [1] Guetig et al. (2003) Learning Input Correlations through Nonlinear
Temporally Asymmetric Hebbian Plasticity. Journal of Neuroscience
 [2] Rubin, J., Lee, D. and Sompolinsky, H. (2001). Equilibrium
properties of temporally asymmetric Hebbian plasticity, PRL 86,364367
 [3] Song, S., Miller, K. D. and Abbott, L. F. (2000). Competitive
Hebbian learning through spiketimingdependent synaptic plasticity,Nature Neuroscience 3:9,919–926
 [4] van Rossum, M. C. W., Bi, GQ and Turrigiano, G. G. (2000).
Stable Hebbian learning from spike timingdependent plasticity, Journal of Neuroscience, 20:23,8812–8821
Parameters
Name 
Physical unit 
Default value 
Description 

d 
ms 
1ms 
Synaptic transmission delay 
lambda 
real 
0.01 

tau_tr_pre 
ms 
20ms 

tau_tr_post 
ms 
20ms 

alpha 
real 
1.0 

mu_plus 
real 
1.0 

mu_minus 
real 
1.0 

Wmax 
real 
100.0 

Wmin 
real 
0.0 
State variables
Name 
Physical unit 
Default value 
Description 

w 
real 
1.0 
Synaptic weight 
Source code
The model source code can be found in the NESTML models repository here: third_factor_stdp_synapse.