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. 2011 Oct 7;6(10):e25339. doi: 10.1371/journal.pone.0025339

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Gilson, Fukai.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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A: Schematic diagram of the neuron (top blue filled circle) stimulated by a pool of 3000 uncorrelated inputs (bottom open circles). B: Evolution of the neuronal output firing rate. C: Evolution of synaptic weights in the case of uncorrelated inputs. The purple traces represent a portion of the input weights recorded every 20 s, the black thin trace corresponds to an individual weight, and the black thick dashed-dotted trace indicates the mean weight over the inputs. Right inset: The mean weight histogram averaged over the learning epoch is plotted in purple and the spot histogram at time 1000 s is represented by the black thin line. D: Comparison between the simulated weight distribution (purple thick curve; it corresponds to the purple curve in C) and the analytical prediction (blue thick curve; same as Fig. 3) with a log scale for the weights (x-axis). The black thin curves represent a Gaussian fit (dashed-dotted) and a lognormal fit (dashed) of the simulated weight distribution, obtained using linear regression. The same baseline parameters as in Fig. 3 have been used here.

Inline graphic — A: Schematic diagram of the neuron (top blue filled circle) stimulated by a pool of 3000 uncorrelated inputs (bottom open circles). B: Evolution of the neuronal output firing rate. C: Evolution of synaptic weights in the case of uncorrelated inputs. The purple traces represent a portion of the input weights recorded every 20 s, the black thin trace corresponds to an individual weight, and the black thick dashed-dotted trace indicates the mean weight over the inputs. Right inset: The mean weight histogram averaged over the learning epoch is plotted in purple and the spot histogram at time 1000 s is represented by the black thin line. D: Comparison between the simulated weight distribution (purple thick curve; it corresponds to the purple curve in C) and the analytical prediction (blue thick curve; same as Fig. 3) with a log scale for the weights (x-axis). The black thin curves represent a Gaussian fit (dashed-dotted) and a lognormal fit (dashed) of the simulated weight distribution, obtained using linear regression. The same baseline parameters as in Fig. 3 have been used here.