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.
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