%0 Figure %A Mensi, Skander %A Hagens, Olivier %A Gerstner, Wulfram %A Pozzorini, Christian %D 2016 %T The iGIF model captures and explains complex forms of adaptation. %U https://plos.figshare.com/articles/figure/The_iGIF_model_captures_and_explains_complex_forms_of_adaptation_/3028636 %R 10.1371/journal.pcbi.1004761.g008 %2 https://ndownloader.figshare.com/files/4731172 %K Rapid Input Fluctuations %K sensitivity %K Nonlinear Threshold Dynamics %K Generalized Linear Model %K coding %K input statistics %K Neocortical Pyramidal Neurons %K neuron %K output spike trains %K input fluctuations %K threshold dynamics adaptively %K model %X

(A) Average effective membrane filters computed with iGIF model parameters extracted from 6 Pyr neurons by increasing μI from 0.05 nA (blue) to 0.5 nA (red, see colorbars in panel D). The passive membrane filter κm(t) (dashed black) is shown for comparison. Inset: average effective conductance as a function of μI. The gray area indicates one standard deviation across neurons and the dashed black line indicates the passive leak conductance gL. (B) Same results as in panel A, but for the average integration filter . Inset: average coupling strength as a function of the mean input μI. Conventions are as in panel A. (C) The effective membrane timescale (red) and the effective timescale of integration (blue) predicted by the iGIF model with parameters extracted from six neurons match the experimental data (black; copied from Fig 2D). Colored lines and gray areas indicate the mean and one standard deviation across neurons. The effective timescales of integration (red) predicted by the iGIF model were obtained by fitting a single-exponential function to . (D) The iGIF model explains the adaptive changes in the spike-history filter hGLM(t) (see Fig 2C). Left: average spike-history filter hGLM(t) obtained by fitting a GLM to artificial data generated by simulating the iGIF model response to fluctuating currents of increasing μI (see colorbar). Right: average theoretical filters computed using iGIF model parameters extracted from 6 Pyr neurons. Because of the approximations involved in the analytical derivation of , the strength of the GLM spike-history filters are underestimated during the firsts ms (see Materials and Methods). (E)-(F) Switching experiment performed in a iGIF model (with parameters extracted from a typical cell) to study the temporal evolution of single neuron adaptation induced by a sudden change in μI. (E) Top: fluctuating current (gray) generated by periodically switching μI (dark gray) between 0.1 nA and 0.27 nA, with cycle period Tcycle = 10 s (only one cycle is shown). Middle: effective timescale of integration as a function of time. Bottom: output firing rate. While spike-frequency adaptation occurs on both fast and slow timescales, changes in triggered by a switch in μI are almost instantaneous. Horizontal black lines indicate (from top to bottom): 0 nA, 0 ms and 0 Hz. (F) Comparison between effective integration filters estimated at different moments in time during the switching experiment (see arrows in panel E). The filters estimated at steady-state (late low, late high; defined as the last 150 ms before the stimulus switch) closely resemble the ones estimated right after the stimulus switch (early low, early high; first 150 ms after the stimulus switch), indicating that adaptive changes in are almost instantaneous. The passive membrane filter κm(t) (dashed black) is shown for comparison. In all panels, input currents were generated according to Eq 8 with σI = 100 pA and τI = 3 ms.

%I PLOS Computational Biology