stdp_synapse – Synapse type for spike-timing dependent plasticity

Description

stdp_synapse 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.

Warning

This synaptic plasticity rule does not take precise spike timing into account. When calculating the weight update, the precise spike time part of the timestamp is ignored.

See also [2], [3], [4].

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

Transmits

SpikeEvent

References

[1]

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

See also

tsodyks_synapse, static_synapse

Examples using this model

• Store and restore a network simulation

• Synapse Collection usage example