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

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Accession:87582
"... 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. ..."
Reference:
1 . Tamosiunaite M, Porr B, Wörgötter F (2007) Self-influencing synaptic plasticity: recurrent changes of synaptic weights can lead to specific functional properties. J Comput Neurosci 23:113-27 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Simplified Models; Active Dendrites; Synaptic Plasticity; Winner-take-all; STDP;
Implementer(s):
Search NeuronDB for information about:  AMPA; NMDA;
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