Command: stdp_synapse

Name:
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.

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 double - Time constant of STDP window, potentiation in ms (tau_minus defined in post-synaptic neuron) lambda double - Step size alpha double - Asymmetry parameter (scales depressing increments as alpha*lambda) mu_plus double - Weight dependence exponent, potentiation mu_minus double - Weight dependence exponent, depression Wmax double - Maximum allowed weight

Transmits:
SpikeEvent

References:
[1] 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

FirstVersion:
March 2006

Author:
Moritz Helias, Abigail Morrison
SeeAlso: synapsedict tsodyks_synapse static_synapse

Source:
/home/abuild/rpmbuild/BUILD/nest-2.4.1/models/stdp_connection.h

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Name:	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.
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 double - Time constant of STDP window, potentiation in ms (tau_minus defined in post-synaptic neuron) lambda double - Step size alpha double - Asymmetry parameter (scales depressing increments as alpha*lambda) mu_plus double - Weight dependence exponent, potentiation mu_minus double - Weight dependence exponent, depression Wmax double - Maximum allowed weight
Transmits:	SpikeEvent
References:	[1] 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
FirstVersion:	March 2006
Author:	Moritz Helias, Abigail Morrison
SeeAlso:	`synapsedict` `tsodyks_synapse` `static_synapse`
Source:	/home/abuild/rpmbuild/BUILD/nest-2.4.1/models/stdp_connection.h