Computational model
Adaptation of Short-Term Plasticity parameters (Esposito et al. 2015)
"The anatomical connectivity among neurons has been experimentally found to be largely non-random across brain areas. This means that certain connectivity motifs occur at a higher frequency than would be expected by chance. Of particular interest, short-term synaptic plasticity properties were found to colocalize with specific motifs: an over-expression of bidirectional motifs has been found in neuronal pairs where short-term facilitation dominates synaptic transmission among the neurons, whereas an over-expression of unidirectional motifs has been observed in neuronal pairs where short-term depression dominates. In previous work we found that, given a network with fixed short-term properties, the interaction between short- and long-term plasticity of synaptic transmission is sufficient for the emergence of specific motifs. Here, we introduce an error-driven learning mechanism for short-term plasticity that may explain how such observed correspondences develop from randomly initialized dynamic synapses. ..."
  • Esposito U, Giugliano M, Vasilaki E (2014) Show Other
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Last Time: 11/13/2015 3:39:23 PM
Reviewer: Tom Morse - MoldelDB admin
Owner: Tom Morse - MoldelDB admin