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

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

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: Tamosiunaite M, Porr B, Worgotter F (2007) Self-influencing synaptic plasticity: Recurrent changes of synaptic weights can lead to specific functional properties. J Comput Neurosci 23:113-127 [PubMed]

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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;

Model files

Help downloading and running models

selfinfluencing_plasticity

 this is the readme for the code associated with the paper:

Tamosiunaite M, Porr B, Worgotter F (2007) Self-influencing synaptic
plasticity: Recurrent changes of synaptic weights can lead to specific
functional properties. J Comput Neurosci 23:113-127

These model files were supplied by M. Tamosiunaite.

See the comments in the main program 'one_cluster.m' for a additional
documentation.

To run type one_cluster at the matlab prompt.  A couple of figures are
created, one of which is similar to Fig 6. in the paper:



The program uses functions:
'histereze.m' - for hysteresis type saturation of weights;
'funh.m' - for mending small weight deviations out of [0,1] interval
due to finite discretization;
'filtras100.m', 'filtras200.m', 'filtras250.m' - filters of signals.

Using three different filter functions is because of mess in
programing, and has to do only with signal length appropriate
matching, while filter shape is determined by function parameters f
and Q.

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