Self-influencing synaptic plasticity (Tamosiunaite et al. 2007)

"... Similar to a previous study (Saudargiene et al., 2004) we employ a differential Hebbian learning rule to emulate spike-timing dependent plasticity and investigate how the interaction of dendritic and back-propagating spikes, as the post-synaptic signals, could influence plasticity. ..."

Model Type: Neuron or other electrically excitable cell; Synapse

Receptors: AMPA; NMDA

Model Concept(s): Simplified Models; Active Dendrites; Synaptic Plasticity; Winner-take-all; STDP

Simulation Environment: MATLAB


Tamosiunaite M, Porr B, Wörgötter F. (2007). Self-influencing synaptic plasticity: recurrent changes of synaptic weights can lead to specific functional properties. Journal of computational neuroscience. 23 [PubMed]

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