Name
Abstract | ||
|---|---|---|
Spike-timing-dependent plasticity is the process by which the strengths of connections between neurons are modified as a result of the precise timing of the action potentials fired by the neurons. We consider a model consisting of one integrate-and-fire neuron receiving excitatory inputs from a large number-here, 1000-of Poisson neurons whose synapses are plastic. When correlations are introduced between the firing times of these input neurons, the distribution of synaptic strengths shows interesting, and apparently low-dimensional, dynamical behaviour. This behaviour is analysed in two different parameter regimes using equation-free techniques, which bypass the explicit derivation of the relevant low-dimensional dynamical system. We demonstrate both coarse projective integration (which speeds up the time integration of a dynamical system) and the use of recently developed data mining techniques to identify the appropriate low-dimensional description of the complex dynamical systems in our model. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s00422-015-0668-0 | Biological Cybernetics |
Keywords | Field | DocType |
Spike-timing-dependent plasticity, Equation-free, Model reduction, Neuronal network | Synapse,Dynamical systems theory,Artificial intelligence,Spike-timing-dependent plasticity,Poisson distribution,Biological neural network,Neuron,Mathematics,Machine learning,Dynamical system,Plasticity | Journal |
Volume | Issue | ISSN |
109 | 6 | 1432-0770 |
Citations | PageRank | References |
0 | 0.34 | 17 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlo R. Laing | 1 | 295 | 41.21 |
| Ioannis G. Kevrekidis | 2 | 494 | 74.95 |