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. 2010 Dec 16;6(12):e1001026. doi: 10.1371/journal.pcbi.1001026

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Schwalger et al.

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 The ISI densities of the Traub-Miles neuron model with a deterministic M-type adaptation current () and white noise driving (Eq. (115) – gray bars) is shown along with an inverse Gaussian (Eq. (64)) with the same mean and CV (dashed lines). To keep the firing rate at about the external driving current was adjusted from top to bottom according to , , , (in ). Noise intensity in units of . B The ISI densities of the Traub-Miles model in the presence of a stochastic M-type adaptation current (Eq. (116) – gray bars) is shown along with an inverse Gaussian (Eq. (64)) with the same mean and CV (dashed line). Here, the external driving current was in all cases .

Inline graphic — A The ISI densities of the Traub-Miles neuron model with a deterministic M-type adaptation current () and white noise driving (Eq. (115) – gray bars) is shown along with an inverse Gaussian (Eq. (64)) with the same mean and CV (dashed lines). To keep the firing rate at about the external driving current was adjusted from top to bottom according to , , , (in ). Noise intensity in units of . B The ISI densities of the Traub-Miles model in the presence of a stochastic M-type adaptation current (Eq. (116) – gray bars) is shown along with an inverse Gaussian (Eq. (64)) with the same mean and CV (dashed line). Here, the external driving current was in all cases .