stdp_synapse

stdp - Synapse model for spike-timing dependent plasticity

Description

stdp_synapse is a synapse with spike-timing 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

References

[1] (1,2)

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 86,364-367

[3]

Song, S., Miller, K. D. and Abbott, L. F. (2000). Competitive Hebbian learning through spike-timing-dependent synaptic plasticity,Nature Neuroscience 3:9,919–926

[4]

van Rossum, M. C. W., Bi, G-Q and Turrigiano, G. G. (2000). Stable Hebbian learning from spike timing-dependent 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
mu_plus	real	1
mu_minus	real	1
Wmax	real	100.0
Wmin	real	0.0

State variables

Name	Physical unit	Default value	Description
w	real	1.0	Synaptic weight
pre_trace	real	0.0
post_trace	real	0.0

Source code

The model source code can be found in the NESTML models repository here: stdp_synapse.