Warning

This is A PREVIEW for NEST 3.0 and NOT an OFFICIAL RELEASE! Some functionality may not be available and information may be incomplete!

stdp_connection_hom – Synapse type for spike-timing dependent plasticity using homogeneous parameters

Description

stdp_synapse_hom is a connector to create synapses with spike time dependent plasticity (as defined in 1). Here the weight dependence exponent can be set separately for potentiation and depression.

Parameters controlling plasticity are identical for all synapses of the model, reducing the memory required per synapse considerably.

Examples:

  • multiplicative STDP 2 mu_plus = mu_minus = 1.0

  • additive STDP 3 mu_plus = mu_minus = 0.0

  • Guetig STDP 1 mu_plus = mu_minus = [0.0,1.0]

  • van Rossum STDP 4 mu_plus = 0.0 mu_minus = 1.0

Parameters

tau_plus

ms

Time constant of STDP window, potentiation (tau_minus defined in postsynaptic neuron)

lambda

real

Step size

alpha

real

Asymmetry parameter (scales depressing increments as alpha*lambda)

mu_plus

real

Weight dependence exponent, potentiation

mu_minus

real

Weight dependence exponent, depression

Wmax

real

Maximum allowed weight

Remarks:

The parameters are common to all synapses of the model and must be set using SetDefaults on the synapse model.

Transmits

SpikeEvent

References

1(1,2)

Guetig et al. (2003). Learning input correlations through nonlinear temporally asymmetric hebbian plasticity. Journal of Neuroscience, 23:3697-3714 DOI: https://doi.org/10.1523/JNEUROSCI.23-09-03697.2003

2

Rubin J, Lee D, Sompolinsky H (2001). Equilibrium properties of temporally asymmetric Hebbian plasticity. Physical Review Letters, 86:364-367. DOI: https://doi.org/10.1103/PhysRevLett.86.364

3

Song S, Miller KD, Abbott LF (2000). Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience 3(9):919-926. DOI: https://doi.org/10.1038/78829

4

van Rossum MCW, Bi G-Q, Turrigiano GG (2000). Stable Hebbian learning from spike timing-dependent plasticity. Journal of Neuroscience, 20(23):8812-8821. DOI: https://doi.org/10.1523/JNEUROSCI.20-23-08812.2000