diff --git a/data_overview_mod.pdf b/data_overview_mod.pdf index 8bc2af6..2a107b6 100644 Binary files a/data_overview_mod.pdf and b/data_overview_mod.pdf differ diff --git a/data_overview_mod.png b/data_overview_mod.png index b9acf8a..ec24f6f 100644 Binary files a/data_overview_mod.png and b/data_overview_mod.png differ diff --git a/data_overview_mod.py b/data_overview_mod.py index ac6db77..ac13fcc 100644 --- a/data_overview_mod.py +++ b/data_overview_mod.py @@ -1,4 +1,4 @@ - +from IPython import embed from matplotlib import gridspec as gridspec, pyplot as plt import numpy as np @@ -30,7 +30,7 @@ def data_overview3(): right = 0.85 ws = 0.75 #print(right) - grid0 = gridspec.GridSpec(3, 2, wspace=ws, bottom=0.07, + grid0 = gridspec.GridSpec(3, 2, wspace=ws, bottom=0.06, hspace=0.45, left=0.1, right=right, top=0.95) ########################## @@ -78,7 +78,7 @@ def data_overview3(): var_item_names = [var_it,var_it,var_it2]#,var_it2]#['Response Modulation [Hz]',] var_types = ['response_modulation','response_modulation','']#,'']#'response_modulation' max_x = max_xs[c] - x_axis_names = ['CV$' + basename_small() +'$','CV$' + label_stimname_small() + '$', 'Response Modulation [Hz]']#$'+basename()+'$,'Fr$'+basename()+'$',] + x_axis_names = ['CV$' + basename_small() +'$','CV$' + label_stimname_small() + '$', 'Response modulation [Hz]']#$'+basename()+'$,'Fr$'+basename()+'$',] #score = scores[0] score_n = ['Perc99/Med', 'Perc99/Med', 'Perc99/Med'] score = scores[c] @@ -156,8 +156,10 @@ def data_overview3(): axx.minorticks_off() axy.set_yticks_blank() + plt_specific_cells(axs, cell_type_here, x_axis[v], frame_file, scores_here[v], marker = ['o',"s"]) tags.append(axx) + #plt.show() counter += 1 #plt.show() diff --git a/susceptibility1.tex.bak b/susceptibility1.tex.bak index eb3c593..8117eb5 100644 --- a/susceptibility1.tex.bak +++ b/susceptibility1.tex.bak @@ -528,8 +528,8 @@ We estimated the second-order susceptibility of P-unit responses using RAM stimu \caption{\label{fig:model_full} Using second-order susceptibility to predict responses to sine-wave stimuli. \figitem[]{A} Absolute value of the second-order susceptibility, \Eqnref{eq:susceptibility}, for both positive and negative frequencies. \susceptf{} was estimated from $N=10^6$ trials of model simulations in the noise-split condition (cell 2013-01-08-aa, see table~\ref{modelparams} for model parameters). White lines indicate zero frequencies. Nonlinear responses at \fsum{} are quantified in the upper right and lower left quadrants. Nonlinear responses at \fdiff{} are quantified in the upper left and lower right quadrants. The baseline firing rate of this cell was at $\fbase=120$\,Hz. The position of the orange/red letters corresponds to the beat frequencies used for the stimulation with pure sine waves in the subsequent panels and indicates the sum/difference of those beat frequencies. \figitem{B--E} Black line -- power spectral density of model simulations in response to stimulation with two pure sine waves, \fone{} and \ftwo, in addition to the receiving fish's own EOD (three-fish scenario). The contrast of beat beats is 0.02. Colored circles highlight the height of selected peaks in the power spectrum. Grey line -- power spectral density of model in the baseline condition. \figitem{B} The sum of the two beat frequencies match \fbase{}. \figitem{C} The difference of \fone{} and \ftwo{} match \fbase{}. \figitem{D} Only the first beat frequency matches \fbase{}. \figitem{C} None of the two beat frequencies matches \fbase{}.} \end{figure*} -However, the second-order susceptibility \Eqnref{eq:susceptibility} is a spectral measure that is based on Fourier transforms and thus is also defined for negative stimulus frequencies. The full \susceptf{} matrix is symmetric with respect to the origin. In the upper-right and lower-left quadrants of \susceptf{}, stimulus-evoked responses at \fsum{} are shown, whereas in the lower-right and upper-left quadrants nonlinear responses at the difference \fdiff{} are shown (\figref{fig:model_full}). The vertical and horizontal lines at \foneb{} and \ftwob{} are very pronounced in the upper-right quadrant of \subfigrefb{fig:model_full}{A} for nonlinear responses at \fsum{} and extend into the upper-left quadrant (representing \fdiff) where they fade out towards more negative $f_1$ frequencies. The peak in the response power-spectrum at \fdiff{} evoked by pure sine-wave stimulation (\subfigrefb{fig:motivation}{D}) is predicted by the horizontal line in the upper-left quadrant (\subfigrefb{fig:model_full}{A}, \citep{Schlungbaum2023}). -Is it possible based on the second-order susceptibility estimated by means of RAM stimuli (\subfigrefb{fig:model_full}{A}) to predict nonlinear responses in a three-fish setting? We can test this by stimulating the same model with two beats with weak amplitudes (\subfigrefb{fig:model_full}{C--F}). If we choose a frequency combination where the sum of the two beat frequencies is equal to the model's baseline firing rate \fbase{}, a peak at the sum of the two beat frequencies appears in the power spectrum of the response (\subfigrefb{fig:model_full}{C}), as expected from \suscept. If instead we choose two beat frequencies that differ by \fbase{}, a peak is present at the difference frequency (\subfigrefb{fig:model_full}{D}). If only one beat frequency is equal to \fbase{}, both a peak at the sum and at the difference frequency is present in the P-unit response (\subfigrefb{fig:model_full}{E}). And if none of these conditions are met, neither a peak at the sum nor at the difference of the two beat frequencies appears (\subfigrefb{fig:model_full}{F}). +However, the second-order susceptibility \Eqnref{eq:susceptibility} is a spectral measure that is based on Fourier transforms and thus is also defined for negative stimulus frequencies. The full \susceptf{} matrix is symmetric with respect to the origin. In the upper-right and lower-left quadrants of \susceptf{}, stimulus-evoked responses at \fsum{} are shown, whereas in the lower-right and upper-left quadrants nonlinear responses at the difference \fdiff{} are shown (\figref{fig:model_full}). The vertical and horizontal lines at \foneb{} and \ftwob{} are very pronounced in the upper-right quadrant of \subfigrefb{fig:model_full}{A} for nonlinear responses at \fsum{} and extend into the upper-left quadrant (representing \fdiff) where they fade out towards more negative $f_1$ frequencies. The peak in the response power-spectrum at \fdiff{} evoked by pure sine-wave stimulation (\subfigrefb{fig:motivation}{D}) is predicted by the horizontal line in the upper-left quadrant (\subfigrefb{fig:model_full}{A}, \citealp{Schlungbaum2023}). +Is it possible based on the second-order susceptibility estimated by means of RAM stimuli (\subfigrefb{fig:model_full}{A}) to predict nonlinear responses in a three-fish setting? We can test this by stimulating the same model with two beats with weak amplitudes (\subfigrefb{fig:model_full}{B--E}). If we choose a frequency combination where the sum of the two beat frequencies is equal to the model's baseline firing rate \fbase{}, a peak at the sum of the two beat frequencies appears in the power spectrum of the response (\subfigrefb{fig:model_full}{B}), as expected from \suscept. If instead we choose two beat frequencies that differ by \fbase{}, a peak is present at the difference frequency (\subfigrefb{fig:model_full}{C}). If only one beat frequency is equal to \fbase{}, both a peak at the sum and at the difference frequency are present in the P-unit response (\subfigrefb{fig:model_full}{D}). And if none of these conditions are met, neither a peak at the sum nor at the difference of the two beat frequencies appears (\subfigrefb{fig:model_full}{E}). \begin{figure*}[tp] \includegraphics[width=\columnwidth]{data_overview_mod.pdf}