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 :ref:`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 ++++++++ :doc:`Synapse `, :doc:`Spike-Timing-Dependent Plasticity `