updated figures

ampullary.py

@@ -53,4 +53,4 @@ if __name__ == '__main__':
             ampullary_punit(cells_plot2=cells_plot2, RAM=False)
     else:
         cells_plot2 = p_units_to_show(type_here='amp')
-        ampullary_punit(titles=['Low-CV ampullary cell,'],cells_plot2=cells_plot2, RAM=False, scale_val = False, add_texts = [0.25,1.3])
+        ampullary_punit(eod_metrice = False, tags_individual = True, isi_delta = 5, titles=['Low-CV ampullary cell,'],cells_plot2=cells_plot2, RAM=False, scale_val = False, add_texts = [0.25,1.3])

cells_suscept.py

@@ -9,4 +9,4 @@ if __name__ == '__main__':
 
 
     cells_plot2 = p_units_to_show(type_here = 'contrasts')
-    ampullary_punit( cells_plot2=[cells_plot2[0]], titles=['Low-CV P-unit,'])
+    ampullary_punit(eod_metrice = False,  cells_plot2=[cells_plot2[0]], isi_delta = 5, titles=['Low-CV P-unit,'],tags_individual = True, xlim_p = [0,1.15])

cells_suscept_high_CV.py

@@ -7,4 +7,4 @@ from IPython import embed
 if __name__ == '__main__':
 
     cells_plot2 = p_units_to_show(type_here='contrasts')
-    ampullary_punit(cells_plot2=[cells_plot2[1]], titles=['High-CV P-unit,', 'Ampullary cell,'],)
+    ampullary_punit(eod_metrice = False, tags_individual = True, isi_delta = 5, cells_plot2=[cells_plot2[1]], titles=['High-CV P-unit,', 'Ampullary cell,'],)

model_and_data.py

@@ -22,7 +22,7 @@ def table_printen(table):
         print(l1)
 
 
-def model_and_data2(width=0.005, nffts=['whole'], powers=[1], cells=["2013-01-08-aa-invivo-1"], show=False,
+def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1], cells=["2013-01-08-aa-invivo-1"], show=False,
                    contrasts=[0], noises_added=[''], D_extraction_method=['additiv_cv_adapt_factor_scaled'],
                    internal_noise=['RAM'], external_noise=['RAM'], level_extraction=[''], receiver_contrast=[1],
                    dendrids=[''], ref_types=[''], adapt_types=[''], c_noises=[0.1], c_signal=[0.9], cut_offs1=[300],
@@ -91,7 +91,7 @@ def model_and_data2(width=0.005, nffts=['whole'], powers=[1], cells=["2013-01-08
         #ypos_x_modelanddata()
         nr = 1
         ax_data, stack_spikes_all, eod_frs = plt_data_susept(fig, grid_data, cells_all, cell_type='p-unit', width=width,
-                                                             cbar_label=True, nr = nr, amp_given = 1,xlabel = False, lp=lp, title=True)
+                                                             cbar_label=True, eod_metrice = eod_metrice, nr = nr, amp_given = 1,xlabel = False, lp=lp, title=True)
         for ax_external in ax_data:
             # ax.set_ylabel(F2_xlabel())
             # remove_xticks(ax)
@@ -170,7 +170,7 @@ def model_and_data2(width=0.005, nffts=['whole'], powers=[1], cells=["2013-01-08
 
             if len(stack)> 0:
                 add_nonlin_title, cbar, fig, stack_plot, im = plt_single_square_modl(ax_external, cell, stack, perc, titles[s],
-                                                                                     width, titles_plot=True,
+                                                                                     width, eod_metrice = eod_metrice, titles_plot=True,
                                                                                      resize=True, nr = nr)
 
                 # if s in [1,3,5]:
@@ -392,17 +392,21 @@ if __name__ == '__main__':
 
     ##########################
     # hier printen wir die table Werte zum kopieren in den Text
-    table = pd.read_csv('print_table_suscept-model_params_suscept_table.csv')
+    path = 'print_table_suscept-model_params_suscept_table.csv'
+    if os.path.exists(path):
+        table = pd.read_csv(path)
         table_printen(table)
 
-    table = pd.read_csv('print_table_all-model_params_suscept_table.csv')
+    path = 'print_table_all-model_params_suscept_table.csv'
+    if os.path.exists(path):
+        table = pd.read_csv()
         print('model big')
         table_printen(table)
     #embed()
 
     ##########################
     #embed()
-    model_and_data2(width=0.005, show=show, D_extraction_method=D_extraction_method,
+    model_and_data2(eod_metrice = False, width=0.005, show=show, D_extraction_method=D_extraction_method,
                    label=r'$\frac{1}{mV^2S}$')  #r'$\frac{1}{mV^2S}$'
 
 

model_and_data__spikes_c_sig_0_a_fe_0.009.csv

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model_and_data_additiv_cv_adapt_factor_scaled_spikes_c_sig_0.9_a_fe_0.009.csv

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model_and_data_additiv_cv_adapt_factor_scaled_spikes_c_sig_0.9_a_fe_0.csv

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+192,1.60755
+193,1.61305
+194,1.62165
+195,1.63025
+196,1.6410500000000001
+197,1.64745
+198,1.6571500000000001
+199,1.6636000000000002
+200,1.6712
+201,1.67975
+202,1.68835
+203,1.6981000000000002
+204,1.70565
+205,1.7152500000000002
+206,1.7217500000000001
+207,1.73035
+208,1.7379
+209,1.7508000000000001
+210,1.75625
+211,1.7638
+212,1.7746000000000002
+213,1.7821500000000001
+214,1.7886000000000002
+215,1.7993000000000001
+216,1.8057500000000002
+217,1.81335
+218,1.8261500000000002
+219,1.83275
+220,1.8403
+221,1.8478
+222,1.8553000000000002
+223,1.8639000000000001
+224,1.87145
+225,1.8823500000000002
+226,1.8908500000000001
+227,1.89735
+228,1.91015
+229,1.9167500000000002
+230,1.9243000000000001
+231,1.9361000000000002
+232,1.94255
+233,1.9480000000000002
+234,1.9587
+235,1.9651500000000002
+236,1.9749
+237,1.9825000000000002
+238,1.991

print_table_suscept-model_params_suscept_table.csv

@@ -0,0 +1,5 @@
+cell,input_scaling,mem_tau,v_offset,noise_strength,tau_a,delta_a,dend_tau,ref_period
+2012-07-03-ak,$10.6$,$1.38$,$-1.32$,$0.001$,$96.05$,$0.01$,$1.18$,$0.12$
+2013-01-08-aa,$4.5$,$1.20$,$0.59$,$0.001$,$37.52$,$0.01$,$1.18$,$0.38$
+2018-05-08-ae,$139.6$,$1.49$,$-21.09$,$0.214$,$123.69$,$0.16$,$3.93$,$1.31$
+median,10.6,1.38,$-1.32$,0.001,96.05,0.01,1.18,0.38

susceptibility1.tex

@@ -587,8 +587,10 @@ In this work, the second-order susceptibility in spiking responses of P-units wa
  %occurring nonlinearities could only be explained based on a  (\foneb{},  \ftwob{}, \fsumb{}, \fdiffb{},
 In this chapter, it was demonstrated that in small homogeneous populations of electrophysiological recorded cells, a diagonal structure appeared when the sum of the input frequencies was equal to \fbase{} in the second-order susceptibility  (\figrefb{cells_suscept}, \figrefb{ampullary}). The size of a population is limited by the possible recording duration in an experiment, but there are no such limitations in P-unit models, where an extended nonlinear structure with diagonals, vertical and horizontal lines at  \fsumb{}, \fdiffb{}, \foneb{} or \ftwob{} appeared with increased stimulus realizations (\figrefb{model_full}). This nonlinear structure is in line with the one predicted in the framework of the nonlinear response theory, which was developed based on LIF models without carrier \citealp{Voronenko2017} and was here confirmed to be valid in models with carrier (\figrefb{model_and_data}).
 
+
+
 \subsection{Noise stimulation approximates the nonlinearity in a three-fish setting}%implying that the
-In this chapter, the nonlinearity of P-units and ampullary cells was retrieved based on white noise stimulation, where all behaviorally relevant frequencies were simultaneously present in the stimulus. The nonlinearity of LIF models (\citealp{Egerland2020}) and of ampullary cells in paddlefish \citealp{Neiman2011fish} has been previously accessed with bandpass limited white noise. 
+In this chapter, the nonlinearity of P-units and ampullary cells was retrieved based on white noise stimulation, where all behaviorally relevant frequencies were simultaneously present in the stimulus. The nonlinearity of ampullary cells in paddlefish \citealp{Neiman2011fish} has been previously accessed with bandpass limited white noise. 
 
 Here it was demonstrated that the second-order susceptibility for the two RAM noise input frequencies \fone{} and \ftwo{} can approximate a three-fish setting, where the driving force for the P-unit are two beats with frequencies \bone{} and \btwo{}. This was confirmed by a low-CV P-unit, where nonlinearities in the P-unit response occurred at the sum and difference of the beat frequencies for pure sine-wave stimulation (\figrefb{motivation}). In this P-unit the nonlinearity appeared in a three-wave setting with \bone{} being close to \fbase{}, corresponding to a frequency combination on the vertical line at \foneb{} in the second-order susceptibility (\subfigrefb{model_full}{B}). This implies that even if only the diagonal structure can be accessed with noise stimulation in the second-order susceptibility in an experiment (\subfigrefb{model_full}{A}), it can be taken as an indicator that the whole nonlinear structure should be present during pure sine-wave stimulation in the same cell. With this RAM stimulation was demonstrated to be an effective method to scan the three-fish or two-beat plane and estimate the whole theoretically predicted nonlinear structure in experimentally recorded cells.
 
@@ -609,13 +611,13 @@ In this chapter strong nonlinear interactions were found in a subpopulation of l
 
 
 %Nonlinear effects only for specific frequency combinations
-\subsection{The readout from P-units in pyramidal cells is heterogeneous}
+\subsection{Heterogeneity of P-units might influence nonlinearity}
 
 %is required to cover all female-intruder combinations} %gain_via_mean  das ist der Frequenz plane faktor
 %associated with nonlinear effects as \fdiffb{} and \fsumb{} were  with the same firing rate  low-CV P-units
 %and \figrefb{ROC_with_nonlin} 
 
-The findings in this work are based on a low-CV P-unit model and might require a selective readout from a homogeneous population of low-CV cells to sustain. Since a receptive field is defined as a patch of adjacent receptors on the fish surface \citealp{Maler2009a} and P-units have heterogeneous baseline properties (CV and \fbasesolid{}) that do not depend on the location of the receptor on the fish body \citealp{Hladnik2023} as a result each pyramidal cell in the ELL will integrate over a heterogeneous and not homogeneous P-unit population. The center-surround receptive field organization of superficial and intermediate layer pyramidal cells in addition increases this heterogeneity of the integrated population. Heterogeneous populations with different baseline properties can be more advantageous for the encoding compared to homogeneous populations, as has been shown in P-units and models \citealp{Beiran2018, Hladnik2023}. 
+The nonlinearity in this work are based on a low-CV P-unit model and might require a selective readout from a homogeneous population of low-CV cells to sustain. Since a receptive field is defined as a patch of adjacent receptors on the fish surface \citealp{Maler2009a} and P-units have heterogeneous baseline properties (CV and \fbasesolid{}) that do not depend on the location of the receptor on the fish body \citealp{Hladnik2023} as a result each pyramidal cell in the ELL will integrate over a heterogeneous and not homogeneous P-unit population. The center-surround receptive field organization of superficial and intermediate layer pyramidal cells in addition increases this heterogeneity of the integrated population. Heterogeneous populations with different baseline properties can be more advantageous for the encoding compared to homogeneous populations, as has been shown in P-units and models \citealp{Beiran2018, Hladnik2023}. 
 
 
 %\subsubsection{A heterogeneous readout is required to cover all female-intruder combinations} 
@@ -633,8 +635,8 @@ A heterogeneous readout might be not only physiologically plausible but also req
 
 
 %These low-frequency modulations of the amplitude modulation are
-\subsection{Encoding of secondary envelopes}%($\n{}=49$)	Whether this population of envelope encoders was in addition bursty was not addressed in the corresponding study \citet{Savard2011}.
+\subsection{Encoding of secondary envelopes}%($\n{}=49$)	Whether this population of envelope encoders was in addition bursty was not addressed in the corresponding study
-The RAM stimulus used in this chapter is an approximation of the three-fish scenario, where the two generated beats are often slowly modulated at the difference between the two beat frequencies \bdiff{}, known as secondary or social envelope \citealp{Stamper2012Envelope}. In previous works it was shown that low-frequency secondary envelopes are extracted not in P-units but downstream of them in the ELL \citealp{Middleton2006} utilizing threshold nonlinear response curves of the involved neuron \citealp{Middleton2007}. This work addressed that only a small class of cells, with very low CVs would encode the social envolope. This small percentage of the low-CV cells would be in line with no P-units found in the work by \citet{Middleton2007}. On the other hand in previous literature the encoding of social envelopes was attributed to a large subpopulationof P-units with stronger nonlinearities, lower firing rates and higher CVs \citealp{Savard2011}. The explanation for these findings might be in another baseline properties of P-units, bursting, firing repeated burst packages of spikes interleaved with quiescence (unpublished work).
+The RAM stimulus used in this chapter is an approximation of the three-fish scenario, where the two generated beats are often slowly modulated at the difference between the two beat frequencies \bdiff{}, known as secondary or social envelope \citealp{Stamper2012Envelope}. In previous works it was shown that low-frequency secondary envelopes are extracted not in P-units but downstream of them in the ELL \citealp{Middleton2006} utilizing threshold nonlinear response curves of the involved neuron \citealp{Middleton2007}. This work addressed that only a small class of cells, with very low CVs would encode the social envolope. This small percentage of the low-CV cells would be in line with no P-units found in the work \citealp{Middleton2007}. On the other hand in previous literature the encoding of social envelopes was attributed to a large subpopulationof P-units with stronger nonlinearities, lower firing rates and higher CVs \citealp{Savard2011}. The explanation for these findings might be in another baseline properties of P-units, bursting, firing repeated burst packages of spikes interleaved with quiescence (unpublished work).
 
 
 \subsection{More fish would decrease second-order susceptibility}%
@@ -642,7 +644,7 @@ When using noise stimulation strong nonlinearity was demonstrated to appear for
 
 
 \subsection{Conclusion} In this work, noise stimulation was confirmed as a method to access the second-order susceptibility in P-unit responses when at least three fish or two beats were present. It was demonstrated that the theory of weakly nonlinear interactions \citealp{Voronenko2017} is valid in P-units where the stimulus is an amplitude modulation of the carrier and not the whole signal.  Nonlinear effects were identified in experimentally recorded low-CV cells primary sensory afferents, the P-units and the ampullary cells. P-units share several features with mammalian
-auditory nerve fibers and such nonlinear effects might also be expected in the auditory system during the encoding of amplitude modulations \citet{Joris2004}.
+auditory nerve fibers and such nonlinear effects might also be expected in the auditory system during the encoding of amplitude modulations \citealp{Joris2004}.
 
 
 
@@ -869,7 +871,8 @@ with $\rho$ a scaling factor that compensates (see below) for the signal transfo
 \end{equation}
 % das stimmt so, das c kommt unter die Wurzel!
 
-A big portion of the total noise was assigned to the signal component ($c_{signal} = 0.9$) and the remaining part to the noise component ($c_{noise} = 0.1$, \subfigrefb{flowchart}\,\panel[iii]{D}). For the application of the Novikov-Furutsu Theorem \citealp{Novikov1965, Furutsu1963} it is critical that both components add up to the initial 100\,$\%$ of the total noise. This is easily achieved in a model without a carrier if the condition $c_{signal}+c_{noise}=1$ is satisfied \citep{Egerland2020}. The situation here is more complicated. To compensate for the transformations the signal undergoes before it enters the LIF core, $\xi_{signal}(t)$ was scaled up by the factor $\rho$ (\eqnref{ram_split}, red in \subfigrefb{flowchart}\,\panel[i]{D}). $\rho$ was found by bisecting the space of possible $\rho$ scaling factors by minimizing the error between the baseline CV (only carrier) and the CV during stimulation with noise split.
+A big portion of the total noise was assigned to the signal component ($c_{signal} = 0.9$) and the remaining part to the noise component ($c_{noise} = 0.1$, \subfigrefb{flowchart}\,\panel[iii]{D}). For the application of the Novikov-Furutsu Theorem \citealp{Novikov1965, Furutsu1963} it is critical that both components add up to the initial 100\,$\%$ of the total noise. This is easily achieved in a model without a carrier if the condition $c_{signal}+c_{noise}=1$ is satisfied. The situation here is more complicated. To compensate for the transformations the signal undergoes before it enters the LIF core, $\xi_{signal}(t)$ was scaled up by the factor $\rho$ (\eqnref{ram_split}, red in \subfigrefb{flowchart}\,\panel[i]{D}). $\rho$ was found by bisecting the space of possible $\rho$ scaling factors by minimizing the error between the baseline CV (only carrier) and the CV during stimulation with noise split.
+
 
 \notejg{Etwas kurz, die Tabelle. Oder benutzen wir hier tatsaechlich nur die zwei Modelle?}
 \begin{table*}[hp!]

susceptibility1.tex.bak

@@ -576,19 +576,21 @@ The P-unit population has higher baseline CVs and lower \nli{} values (\subfigre
 
 
 \section{Discussion}
-In this work, the second-order susceptibility in spiking responses of P-units was characterized for the scenario where at least three fish are present. A low-CV subpopulation of P-units in \lepto{} was demonstrated to have increased nonlinearity values when \fone{}, \ftwo{}, \fsum{} or \fdiff{} were equal to the mean baseline firing rate \fbase{}. Ampullary cells, with even lower CVs, exhibited even stronger nonlinearity. These nonlinear structures in low-CV cells confirmed the predictions from the weakly nonlinear theory  \citet{Voronenko2017}. High-CV P-units did not exhibit any nonlinear interactions. The nonlinearities appearing for the noise frequencies \fone{}, \ftwo{} were confirmed as a proxy for nonlinearities that might arise in response to the beat frequencies \bone{}, \btwo{}, when at least three fish are present.
+In this work, the second-order susceptibility in spiking responses of P-units was characterized for the scenario where at least three fish are present. A low-CV subpopulation of P-units in \lepto{} was demonstrated to have increased nonlinearity values when \fone{}, \ftwo{}, \fsum{} or \fdiff{} were equal to the mean baseline firing rate \fbase{}. Ampullary cells, with even lower CVs, exhibited even stronger nonlinearity. These nonlinear structures in low-CV cells confirmed the predictions from the weakly nonlinear theory \citealp{Voronenko2017}. High-CV P-units did not exhibit any nonlinear interactions. The nonlinearities appearing for the noise frequencies \fone{}, \ftwo{} were confirmed as a proxy for nonlinearities that might arise in response to the beat frequencies \bone{}, \btwo{}, when at least three fish are present.
 
 
 
 %\fsum{} or \fdiff{} were equal to \fbase{} \bsum{} or \bdiff{}, 
 
-\subsection{Methodological implications}%implying that the
+%\subsection{Methodological implications}%implying that the
-\subsubsection{Full nonlinear structure present in LIF models with and without carrier}%implying that the
+\subsection{Full nonlinear structure present in LIF models with and without carrier}%implying that the
  %occurring nonlinearities could only be explained based on a  (\foneb{},  \ftwob{}, \fsumb{}, \fdiffb{},
-In this chapter, it was demonstrated that in small homogeneous populations of electrophysiological recorded cells, a diagonal structure appeared when the sum of the input frequencies was equal to \fbase{} in the second-order susceptibility  (\figrefb{cells_suscept}, \figrefb{ampullary}). The size of a population is limited by the possible recording duration in an experiment, but there are no such limitations in P-unit models, where an extended nonlinear structure with diagonals, vertical and horizontal lines at  \fsumb{}, \fdiffb{}, \foneb{} or \ftwob{} appeared with increased stimulus realizations (\figrefb{model_full}). This nonlinear structure is in line with the one predicted in the framework of the nonlinear response theory, which was developed based on LIF models without carrier (\figrefb{plt_RAM_didactic2}, \citealp{Voronenko2017}) and was here confirmed to be valid in models with carrier (\figrefb{model_and_data}).
+In this chapter, it was demonstrated that in small homogeneous populations of electrophysiological recorded cells, a diagonal structure appeared when the sum of the input frequencies was equal to \fbase{} in the second-order susceptibility  (\figrefb{cells_suscept}, \figrefb{ampullary}). The size of a population is limited by the possible recording duration in an experiment, but there are no such limitations in P-unit models, where an extended nonlinear structure with diagonals, vertical and horizontal lines at  \fsumb{}, \fdiffb{}, \foneb{} or \ftwob{} appeared with increased stimulus realizations (\figrefb{model_full}). This nonlinear structure is in line with the one predicted in the framework of the nonlinear response theory, which was developed based on LIF models without carrier \citealp{Voronenko2017} and was here confirmed to be valid in models with carrier (\figrefb{model_and_data}).
 
-\subsubsection{Noise stimulation approximates the nonlinearity in a three-fish setting}%implying that the
+
-In this chapter, the nonlinearity of P-units and ampullary cells was retrieved based on white noise stimulation, where all behaviorally relevant frequencies were simultaneously present in the stimulus. The nonlinearity of LIF models (\citealp{Egerland2020}) and of ampullary cells in paddlefish \citealp{Neiman2011fish} has been previously accessed with bandpass limited white noise. 
+
+\subsection{Noise stimulation approximates the nonlinearity in a three-fish setting}%implying that the
+In this chapter, the nonlinearity of P-units and ampullary cells was retrieved based on white noise stimulation, where all behaviorally relevant frequencies were simultaneously present in the stimulus. The nonlinearity of ampullary cells in paddlefish \citealp{Neiman2011fish} has been previously accessed with bandpass limited white noise. 
 
 Here it was demonstrated that the second-order susceptibility for the two RAM noise input frequencies \fone{} and \ftwo{} can approximate a three-fish setting, where the driving force for the P-unit are two beats with frequencies \bone{} and \btwo{}. This was confirmed by a low-CV P-unit, where nonlinearities in the P-unit response occurred at the sum and difference of the beat frequencies for pure sine-wave stimulation (\figrefb{motivation}). In this P-unit the nonlinearity appeared in a three-wave setting with \bone{} being close to \fbase{}, corresponding to a frequency combination on the vertical line at \foneb{} in the second-order susceptibility (\subfigrefb{model_full}{B}). This implies that even if only the diagonal structure can be accessed with noise stimulation in the second-order susceptibility in an experiment (\subfigrefb{model_full}{A}), it can be taken as an indicator that the whole nonlinear structure should be present during pure sine-wave stimulation in the same cell. With this RAM stimulation was demonstrated to be an effective method to scan the three-fish or two-beat plane and estimate the whole theoretically predicted nonlinear structure in experimentally recorded cells.
 
@@ -600,26 +602,26 @@ Here it was demonstrated that the second-order susceptibility for the two RAM no
 %. It could be shown that if the nonlinearity at \fsumb{} could be found with a low stimulus repeat number in electrophysiologically recorded P-units, 
 
 
+%\subsection{Nonlinearity and CV}%
 \subsection{Nonlinearity and CV}%
-\subsubsection{Nonlinearity and CV}%
 In this chapter, the CV has been identified as an important factor influencing nonlinearity in the spiking response, with strong nonlinearity in low-CV cells  (\subfigrefb{data_overview_mod}{A--B}). These findings of strong nonlinearity in low-CV cells are in line with previous literature, where it has been proposed that noise linearizes the system \citealp{Roddey2000, Chialvo1997, Voronenko2017}. More noise has been demonstrated to increase the CV and reduce nonlinear phase-locking in vestibular afferents \citealp{Schneider2011}. Reduced noise of low-CV cells has been associated with stronger nonlinearity in pyramidal cells of the ELL \citealp{Chacron2006}. 
 
-\subsubsection{Ampullary cells}%
+\subsection{Ampullary cells}%
 In this chapter strong nonlinear interactions were found in a subpopulation of low-CV P-units and low-CV ampullary cells (\subfigrefb{data_overview_mod}{A, B}). Ampullary cells have lower CVs than P-units, do not phase-lock to the EOD, and have unimodal ISI distributions. With this, ampullary cells have very similar properties as the LIF models, where the theory of weakly nonlinear interactions was developed on \citealp{Voronenko2017}. Almost all here investigated ampullary cells had pronounced nonlinearity bands in the second-order susceptibility for small stimulus amplitudes (\figrefb{ampullary}). With this ampullary cells are an experimentally recorded cell population close to the LIF models that fully meets the theoretical predictions of low-CV cells having very strong nonlinearities \citealp{Voronenko2017}. Ampullary cells encode low-frequency changes in the environment e.g. prey movement \citealp{Engelmann2010, Neiman2011fish}. The activity of a prey swarm of \textit{Daphnia} strongly resembles Gaussian white noise \citealp{Neiman2011fish}, as it has been used in this chapter. How such nonlinear effects in ampullary cells might influence prey detection could be addressed in further studies. 
 
 
 %Nonlinear effects only for specific frequency combinations
-\subsection{The readout from P-units in pyramidal cells is heterogeneous}
+\subsection{Heterogeneity of P-units might influence nonlinearity}
 
 %is required to cover all female-intruder combinations} %gain_via_mean  das ist der Frequenz plane faktor
 %associated with nonlinear effects as \fdiffb{} and \fsumb{} were  with the same firing rate  low-CV P-units
 %and \figrefb{ROC_with_nonlin} 
 
-The findings in this work are based on a low-CV P-unit model and might require a selective readout from a homogeneous population of low-CV cells to sustain. Since a receptive field is defined as a patch of adjacent receptors on the fish surface \citealp{Maler2009a} and P-units have heterogeneous baseline properties (CV and \fbasesolid{}) that do not depend on the location of the receptor on the fish body \citealp{Hladnik2023} as a result each pyramidal cell in the ELL will integrate over a heterogeneous and not homogeneous P-unit population. The center-surround receptive field organization of superficial and intermediate layer pyramidal cells in addition increases this heterogeneity of the integrated population. Heterogeneous populations with different baseline properties can be more advantageous for the encoding compared to homogeneous populations, as has been shown in P-units and models \citealp{Beiran2018, Hladnik2023}. 
+The nonlinearity in this work are based on a low-CV P-units. To sustain on a population level a selective readout from a homogeneous population of low-CV cells might be required. Since a receptive field is defined as a patch of adjacent receptors on the fish surface \citealp{Maler2009a} and P-units have heterogeneous baseline properties (CV and \fbasesolid{}) that do not depend on the location of the receptor on the fish body \citealp{Hladnik2023} as a result each pyramidal cell in the ELL will integrate over a heterogeneous and not homogeneous P-unit population. The center-surround receptive field organization of superficial and intermediate layer pyramidal cells in addition increases this heterogeneity of the integrated population. Heterogeneous populations with different baseline properties can be more advantageous for the encoding compared to homogeneous populations, as has been shown in P-units and models \citealp{Beiran2018, Hladnik2023}. 
 
 
 %\subsubsection{A heterogeneous readout is required to cover all female-intruder combinations} 
-A heterogeneous readout might be not only physiologically plausible but also required to address the electrosensory cocktail party for all female-intruder combinations. In this chapter, the improved intruder detection was present only for specific beat frequencies (\figrefb{ROC_with_nonlin}), corresponding to findings from previous literature \citealp{Schlungbaum2023}. If pyramidal cells would integrate only from P-units with the same mean baseline firing rate \fbasesolid{} not all female-male encounters, relevant for the context of the electrosensory cocktail party, could be covered (black square, \subfigrefb{ROC_with_nonlin}{C}). Only a heterogeneous population with different \fbasesolid{} (50--450\,Hz, \citealp{Grewe2017, Hladnik2023}) could lead to a vertical displacement of the improved intruder detection (red diagonals in \subfigrefb{ROC_with_nonlin}{C}). Weather integrating from such a heterogeneous population with different \fbasesolid{} would cover this behaviorally relevant range in the electrosensory cocktail party should be addressed in further studies. 
+A heterogeneous readout might be not only physiologically plausible but also required if nonlinear effects would be required independent of \fbase{}. If e.g. nonlinear effects would be considered to address the electrosensory cocktail party, a three fish setting where a detection of a faint signal is addressed for different fish situations. In this chapter, the improved intruder detection was present only for specific beat frequencies (\figrefb{ROC_with_nonlin}), corresponding to findings from previous literature \citealp{Schlungbaum2023}. If pyramidal cells would integrate only from P-units with the same mean baseline firing rate \fbasesolid{} not all female-male encounters, relevant for the context of the electrosensory cocktail party, could be covered (black square, \subfigrefb{ROC_with_nonlin}{C}). Only a heterogeneous population with different \fbasesolid{} (50--450\,Hz, \citealp{Grewe2017, Hladnik2023}) could lead to a vertical displacement of the improved intruder detection (red diagonals in \subfigrefb{ROC_with_nonlin}{C}). Weather integrating from such a heterogeneous population with different \fbasesolid{} would cover this behaviorally relevant range in the electrosensory cocktail party should be addressed in further studies. 
 
 %
 %Second-order susceptibility was strongest the lower the CV of the use LIF model \citealp{Voronenko2017}.  
@@ -633,8 +635,8 @@ A heterogeneous readout might be not only physiologically plausible but also req
 
 
 %These low-frequency modulations of the amplitude modulation are
-\subsection{Encoding of secondary envelopes}%($\n{}=49$)	Whether this population of envelope encoders was in addition bursty was not addressed in the corresponding study \citet{Savard2011}.
+\subsection{Encoding of secondary envelopes}%($\n{}=49$)	Whether this population of envelope encoders was in addition bursty was not addressed in the corresponding study
-The RAM stimulus used in this chapter is an approximation of the three-fish scenario, where the two generated beats are often slowly modulated at the difference between the two beat frequencies \bdiff{}, known as secondary or social envelope \citealp{Stamper2012Envelope}. In previous works it was shown that low-frequency secondary envelopes are extracted not in P-units but downstream of them in the ELL \citealp{Middleton2006} utilizing threshold nonlinear response curves of the involved neuron \citealp{Middleton2007}. This work addressed that only a small class of cells, with very low CVs would encode the social envolope. This small percentage of the low-CV cells would be in line with no P-units found in the work by \citet{Middleton2007}. On the other hand in previous literature the encoding of social envelopes was attributed to a large subpopulationof P-units with stronger nonlinearities, lower firing rates and higher CVs \citealp{Savard2011}. The explanation for these findings might be in another baseline properties of P-units, bursting, firing repeated burst packages of spikes interleaved with quiescence (unpublished work).
+The RAM stimulus used in this chapter is an approximation of the three-fish scenario, where the two generated beats are often slowly modulated at the difference between the two beat frequencies \bdiff{}, known as secondary or social envelope \citealp{Stamper2012Envelope}. In previous works it was shown that low-frequency secondary envelopes are extracted not in P-units but downstream of them in the ELL \citealp{Middleton2006} utilizing threshold nonlinear response curves of the involved neuron \citealp{Middleton2007}. This work addressed that only a small class of cells, with very low CVs would encode the social envolope. This small percentage of the low-CV cells would be in line with no P-units found in the work \citealp{Middleton2007}. On the other hand in previous literature the encoding of social envelopes was attributed to a large subpopulationof P-units with stronger nonlinearities, lower firing rates and higher CVs \citealp{Savard2011}. The explanation for these findings might be in another baseline properties of P-units, bursting, firing repeated burst packages of spikes interleaved with quiescence (unpublished work).
 
 
 \subsection{More fish would decrease second-order susceptibility}%
@@ -642,7 +644,7 @@ When using noise stimulation strong nonlinearity was demonstrated to appear for
 
 
 \subsection{Conclusion} In this work, noise stimulation was confirmed as a method to access the second-order susceptibility in P-unit responses when at least three fish or two beats were present. It was demonstrated that the theory of weakly nonlinear interactions \citealp{Voronenko2017} is valid in P-units where the stimulus is an amplitude modulation of the carrier and not the whole signal.  Nonlinear effects were identified in experimentally recorded low-CV cells primary sensory afferents, the P-units and the ampullary cells. P-units share several features with mammalian
-auditory nerve fibers and such nonlinear effects might also be expected in the auditory system during the encoding of amplitude modulations \citet{Joris2004}.
+auditory nerve fibers and such nonlinear effects might also be expected in the auditory system during the encoding of amplitude modulations \citealp{Joris2004}.
 
 
 
@@ -869,7 +871,8 @@ with $\rho$ a scaling factor that compensates (see below) for the signal transfo
 \end{equation}
 % das stimmt so, das c kommt unter die Wurzel!
 
-A big portion of the total noise was assigned to the signal component ($c_{signal} = 0.9$) and the remaining part to the noise component ($c_{noise} = 0.1$, \subfigrefb{flowchart}\,\panel[iii]{D}). For the application of the Novikov-Furutsu Theorem \citealp{Novikov1965, Furutsu1963} it is critical that both components add up to the initial 100\,$\%$ of the total noise. This is easily achieved in a model without a carrier if the condition $c_{signal}+c_{noise}=1$ is satisfied \citep{Egerland2020}. The situation here is more complicated. To compensate for the transformations the signal undergoes before it enters the LIF core, $\xi_{signal}(t)$ was scaled up by the factor $\rho$ (\eqnref{ram_split}, red in \subfigrefb{flowchart}\,\panel[i]{D}). $\rho$ was found by bisecting the space of possible $\rho$ scaling factors by minimizing the error between the baseline CV (only carrier) and the CV during stimulation with noise split.
+A big portion of the total noise was assigned to the signal component ($c_{signal} = 0.9$) and the remaining part to the noise component ($c_{noise} = 0.1$, \subfigrefb{flowchart}\,\panel[iii]{D}). For the application of the Novikov-Furutsu Theorem \citealp{Novikov1965, Furutsu1963} it is critical that both components add up to the initial 100\,$\%$ of the total noise. This is easily achieved in a model without a carrier if the condition $c_{signal}+c_{noise}=1$ is satisfied. The situation here is more complicated. To compensate for the transformations the signal undergoes before it enters the LIF core, $\xi_{signal}(t)$ was scaled up by the factor $\rho$ (\eqnref{ram_split}, red in \subfigrefb{flowchart}\,\panel[i]{D}). $\rho$ was found by bisecting the space of possible $\rho$ scaling factors by minimizing the error between the baseline CV (only carrier) and the CV during stimulation with noise split.
+
 
 \notejg{Etwas kurz, die Tabelle. Oder benutzen wir hier tatsaechlich nur die zwei Modelle?}
 \begin{table*}[hp!]

utils_all_down.py

@@ -5422,7 +5422,7 @@ def plt_50_Hz_noise(ax, cutoff, power_noise_color='blue', ):
 
 
 def plt_triangle(ax, fr, fr_stim, eod_fr, cutoff, eod_fr_half_color='darkorange', line_length = 1 / 4, lines=False, power_noise_color='blue',
-                 fr_color='magenta', nr = 3, stim_triangle=False, eod_fr_color='crimson', fr_stim_color='darkred'):#[1, 0.4, 0]
+                 fr_color='magenta', eod_metrice = True, nr = 3, stim_triangle=False, eod_fr_color='crimson', fr_stim_color='darkred'):#[1, 0.4, 0]
     #print(fr_color)
     # half_triangle(ax, counter, fr, color = 'red', label = 'Sum Fr')
     # embed()
@@ -5438,6 +5438,7 @@ def plt_triangle(ax, fr, fr_stim, eod_fr, cutoff, eod_fr_half_color='darkorange'
         quater_triangle(ax, fr_stim * 2, cutoff, color=fr_stim_color, label='')
         quater_triangle(ax, fr_stim * 3, cutoff, color=fr_stim_color, label='')
 
+    if eod_metrice:
         ax.plot([0, eod_fr / 4], [eod_fr / 2, eod_fr / 4, ], color=eod_fr_half_color,
                 label='', linestyle='--')
         ax.plot([0, eod_fr / 4], [eod_fr / 2, eod_fr / 4, ], color=eod_fr_half_color,

utils_paper.py

@@ -2249,7 +2249,7 @@ def retrieve_mat_plot(stack_final):
     return new_keys, stack_plot
 
 
-def square_func2(ax, stack_final, s=0, nr = 3, fr = None, cbar_do=True, perc=True, line_length = 1 / 4, add_nonlin_title = None):
+def square_func2(ax, stack_final, s=0, nr = 3,eod_metrice = True,  fr = None, cbar_do=True, perc=True, line_length = 1 / 4, add_nonlin_title = None):
 
 
     new_keys, stack_plot = convert_csv_str_to_float(stack_final)
@@ -2293,7 +2293,7 @@ def square_func2(ax, stack_final, s=0, nr = 3, fr = None, cbar_do=True, perc=Tru
     ax[s].set_xlim(mat.index[0], mat.index[-1])
     ax[s].set_ylim(mat.columns[0], mat.columns[-1])
     #embed()
-    plt_triangle(ax[s], fr, np.mean(fr2), eod_fr, new_keys[-1], nr = nr, line_length = line_length)# eod_fr_half_color='purple', power_noise_color='blue',
+    plt_triangle(ax[s], fr, np.mean(fr2), eod_fr, new_keys[-1], nr = nr, eod_metrice = eod_metrice, line_length = line_length)# eod_fr_half_color='purple', power_noise_color='blue',
                  #fr_color='red', eod_fr_color='magenta', fr_stim_color='darkred'
     #embed()
     if cbar_do:
@@ -2836,7 +2836,7 @@ def plt_squares_special(params, col_desired=2, var_items=['contrasts'], show=Fal
             # save_all(0, individual_tag, show, '')
 
 
-def plt_single_square_modl(ax, cell, model, perc, titles, width,nr = 3,  titles_plot=False, resize=False):
+def plt_single_square_modl(ax, cell, model, perc, titles, width,eod_metrice = True, nr = 3,  titles_plot=False, resize=False):
     try:
         model_show = model[(model.cell == cell)]
     except:
@@ -2881,7 +2881,7 @@ def plt_single_square_modl(ax, cell, model, perc, titles, width,nr = 3,  titles_
         im = plt_RAM_perc(ax, perc, stack_plot)
 
         plt_triangle(ax, model_show.fr.iloc[0], np.round(model_show.fr_stim.iloc[0]),
-                     model_show.eod_fr.iloc[0], 300, nr = nr)
+                     model_show.eod_fr.iloc[0], 300, nr = nr, eod_metrice = eod_metrice)
         ax.set_aspect('equal')
         fig = plt.gcf()
         cbar, left, bottom, width, height = colorbar_outside(ax, im, fig, add=0, shrink=0.6, width=width)  # 0.02
@@ -4001,7 +4001,7 @@ def plt_data_up(cell, ax, fig, grid, cells_chosen, cell_type='p-unit', width=0.0
     return ax_data
 
 
-def plt_data_susept(fig, grid, cells_chosen, amp_given = None, nr = 3, cell_type='p-unit',xlabel = True, lp=10, title=True, cbar_label=True, width=0.005):
+def plt_data_susept(fig, grid, cells_chosen, eod_metrice = True, amp_given = None, nr = 3, cell_type='p-unit',xlabel = True, lp=10, title=True, cbar_label=True, width=0.005):
     file_names_exclude = get_file_names_exclude(cell_type)
 
     if len(cells_chosen) > 0:
@@ -4016,14 +4016,14 @@ def plt_data_susept(fig, grid, cells_chosen, amp_given = None, nr = 3, cell_type
         ax_data.append(ax)
 
         eod_fr, stack_spikes = plt_data_suscept_single(ax, cbar_label, cell, cells, f, fig, file_names_exclude, lp,
-                                                       title, width, nr = nr, amp_given = amp_given, xlabel = xlabel)
+                                                       title, width, nr = nr,eod_metrice = eod_metrice, amp_given = amp_given, xlabel = xlabel)
 
         stack_spikes_all.append(stack_spikes)
         eod_frs.append(eod_fr)
     return ax_data, stack_spikes_all, eod_frs
 
 
-def plt_data_suscept_single(ax, cbar_label, cell, cells, f, fig, file_names_exclude, lp, title, width, nr = 3, xlabel = True,amp_given = None):
+def plt_data_suscept_single(ax, cbar_label, cell, cells, f, fig, file_names_exclude, lp, title, width,eod_metrice = True, nr = 3, xlabel = True,amp_given = None):
     if cell == '2012-07-03-ak-invivo-1':
         save_name = 'noise_data9_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s'  # _burst_corr
     else:
@@ -4126,7 +4126,7 @@ def plt_data_suscept_single(ax, cbar_label, cell, cells, f, fig, file_names_excl
                 ax.set_xlim(0, 300)
                 ax.set_ylim(0, 300)
                 ax.set_aspect('equal')
-                plt_triangle(ax, fr, fr_stim, eod_fr, new_keys[-1],nr = nr, lines=False)
+                plt_triangle(ax, fr, fr_stim, eod_fr, new_keys[-1],nr = nr,eod_metrice = eod_metrice, lines=False)
                 ## 
 
                 set_clim_same_here([im], mats=[mat], lim_type='up')
@@ -4257,7 +4257,7 @@ def plt_psds_in_one_squares_next(aa, add, amp, amps_defined, axds, axes, axis, a
                                  power_type=False, peaks_extra=False, zorder=1, alpha=1, extra_input=False, fr=None,
                                  title_square='', fr_diag = None, nr = 1, line_length=1 / 4, text_scalebar=False, xpos_xlabel=-0.2,
                                  add_nonlin_title=None,amp_give = True, color='grey', log_transfer=False, axo2=None, axd2=None,
-                                 axi=None, iterate_var=[0, 1], normval = 1):
+                                 axi=None,eod_metrice = True,  iterate_var=[0, 1], normval = 1):
     # if aa == 0:
     if not fr_diag:
         fr_diag = fr
@@ -4332,7 +4332,7 @@ def plt_psds_in_one_squares_next(aa, add, amp, amps_defined, axds, axes, axis, a
     #embed()
     mat, test_limits, im, add_nonlin_title  = plt_square_here(aa, amp, amps_defined, ax_square, c, cells_plot, ims,
                                            stack_final1, [], perc=False, cbar_true=cbar_true, xpos=0, ypos=1.05,
-                                           color=color,fr = fr,nr = nr, amp_give = amp_give, title_square = title_square, line_length = line_length,  ha='left',xpos_xlabel  = xpos_xlabel , alpha=alpha,  add_nonlin_title  =  add_nonlin_title )
+                                           color=color,fr = fr, eod_metrice = eod_metrice, nr = nr, amp_give = amp_give, title_square = title_square, line_length = line_length,  ha='left',xpos_xlabel  = xpos_xlabel , alpha=alpha,  add_nonlin_title  =  add_nonlin_title )
 
     ims.append(im)
 
@@ -5040,9 +5040,9 @@ def plt_cellbody_eigen(grid1, frame, amps_desired, save_names, cells_plot, cell_
 
 # plt_cellbody_singlecell
 def plt_cellbody_singlecell(grid1, frame, amps_desired, save_names, cells_plot, cell_type_type, plus=1, ax3=[], xlim=[],
-                            burst_corr='_burst_corr_individual', RAM=True,
+                            burst_corr='_burst_corr_individual', RAM=True,isi_delta = None,
                             titles=['Low CV P-unit', 'High CV P-unit', 'Ampullary cell'],
-                            peaks_extra=[False, False, False], add_texts = [0.25,0], scale_val = False):
+                            peaks_extra=[False, False, False], eod_metrice = True,  tags_individual = False, xlim_p = [0,1.1], add_texts = [0.25,0], scale_val = False):
     colors = colors_overview()
     # axos = []
     axis = []
@@ -5140,7 +5140,7 @@ def plt_cellbody_singlecell(grid1, frame, amps_desired, save_names, cells_plot,
                                 eod_fr, file_name, grid_lower, ims, load_name, save_names, stack_file, wss, xlim,
                                 power_type=power_type,fr = fr, peaks_extra=peaks_extra[c], zorder=zorders[aa], alpha=alpha,
                                 extra_input=extra_input, xpos_xlabel=xpos_xlabel, add_nonlin_title=add_nonlin_title,
-                                color=colors[cell_type], axo2=axo2, axd2=axd2, axi=axi, iterate_var=amps_defined, normval = normval)
+                                color=colors[cell_type], axo2=axo2, axd2=axd2, axi=axi, iterate_var=amps_defined, normval = normval, eod_metrice = eod_metrice)
                             diag_vals.append(np.median(diagonals_prj_l))
                             mats.append(mat)
                         else:
@@ -5268,10 +5268,12 @@ def plt_cellbody_singlecell(grid1, frame, amps_desired, save_names, cells_plot,
                     #                                          hspace=0.25)
                     ax_isi = plt.subplot(grid_p[0])
                     ax_p = plt.subplot(grid_p[1])
-                    xlim_p = [0,1.1]
+
                     ax_isi = base_cells_susept(ax_isi, ax_p, c, cell, cell_type, cells_plot, colors, eod_fr, frame,
                                                isi, right, spikes_base, stack,xlim_p,add_texts =add_texts, titles = titles, peaks = True)
 
+                    if isi_delta:
+                        ax_isi.set_xticks_delta(isi_delta)
                 # tags.insert(0, ax_p)
                 tags.insert(0, ax_p)
                 tags.insert(0, ax_isi)
@@ -5291,7 +5293,11 @@ def plt_cellbody_singlecell(grid1, frame, amps_desired, save_names, cells_plot,
     try:
         #embed()
         if len(cells_plot) == 1:
+            if tags_individual:
                 tags_susept_pictures(tags_cell[0],xoffs=np.array([-4.7, -3.2,-3.2, -4.7,-6.3, -2.7,-3.2]), yoffs=np.array([3, 3, 3, 5.5,  5.5,  5.5,  5.5]))
+            else:
+                tags_susept_pictures(tags_cell, yoffs=np.array([3, 3, 5.5, 5.5, 5.5, 5.5]))
+
         else:
             tags_susept_pictures(tags_cell)
     except:
@@ -5303,7 +5309,7 @@ def base_cells_susept(ax_isi, ax_p, c, cell, cell_type, cells_plot, colors, eod_
                       stack, xlim,texts_left = [0.25,0], add_texts = [0.25,0], titles=['', '', '', '', ''], pos = -0.25, peaks = False, fr_name = '$f_{Base}$',xlim_i = [0, 16]):
 
 
-    ax_isi.text(-0.2, 0.5, 'Baseline', rotation=90, ha='center',  va='center', transform=ax_isi.transAxes)
+    #ax_isi.text(-0.2, 0.5, 'Baseline', rotation=90, ha='center',  va='center', transform=ax_isi.transAxes)
     plt_susept_isi_base(c, cell_type, cells_plot, 'grey', ax_isi, isi, xlim=xlim_i,
                         clip_on=True)  # colors[str(cell_type)]
 
@@ -5632,7 +5638,7 @@ def labels_for_psds(axd2, axi, axo2, extra_input, right='middle', chi_pos = -0.3
 
 
 def trasnfer_ylabel():
-    return '$|\mathcal{\chi}_{1}|$'#'$|\chi_{1}|$'#r'$|\mathcal{X}_{1}|$\,[Hz]'#' '
+    return '$|\mathcal{\chi}_{1}|\,$[Hz]'#'$|\chi_{1}|$'#r'$|\mathcal{X}_{1}|$\,[Hz]'#' '
 
 
 def get_base_params(cell, cell_type_type, frame):
@@ -5909,7 +5915,7 @@ def get_max_several_amp_squares(add, amp, amps_defined, files, load_name, stack_
 
 
 def plt_square_here(aa, amp, amps_defined, ax_square, c, cells_plot, ims, stack_final1, xlim, line_length = 1 / 4, alpha=1, cbar_true=True,
-                    perc=True, amp_give = True, ha='right',nr = 3, fr = None,  title_square = '', xpos_xlabel =-0.2, ypos=0.05, xpos=0.1, color='white', add_nonlin_title = None):
+                    perc=True, amp_give = True, eod_metrice = True, ha='right',nr = 3, fr = None,  title_square = '', xpos_xlabel =-0.2, ypos=0.05, xpos=0.1, color='white', add_nonlin_title = None):
     if aa == len(amps_defined) - 1:
         cbar_do = False
     else:
@@ -5917,7 +5923,7 @@ def plt_square_here(aa, amp, amps_defined, ax_square, c, cells_plot, ims, stack_
     # print('cbar'+str(cbar_do))
     print(add_nonlin_title)
     #embed()
-    cbar, mat, im, add_nonlin_title = square_func2([ax_square], stack_final1,fr = fr, nr = nr, line_length = line_length, cbar_do=cbar_do, perc=perc, add_nonlin_title= add_nonlin_title)
+    cbar, mat, im, add_nonlin_title = square_func2([ax_square], stack_final1, eod_metrice = eod_metrice, fr = fr, nr = nr, line_length = line_length, cbar_do=cbar_do, perc=perc, add_nonlin_title= add_nonlin_title)
     ims.append(im)
     if xlim:
         ax_square.set_xlim(xlim)
@@ -6033,8 +6039,8 @@ def plt_diagonal(alpha, axd, color, db, eod_fr, fr, mat, peaks_extra, xlim, zord
     axd.plot(axis_d / normval, diagonals_prj_l, color=color, alpha=alpha-0.05, zorder=zorder)
     if peaks_extra:
         #try:
-        axd.axhline(np.median(diagonals_prj_l), linewidth=0.45, linestyle='--', color=color,
+        axd.axhline(np.median(diagonals_prj_l), linewidth=0.75, linestyle='--', color=color,
-                    alpha=alpha, zorder = zorder +1)
+                    alpha=alpha, zorder = zorder +1)#0.45
         plt_peaks_several(['fr'], [fr / normval], [diagonals_prj_l], 0, axd, diagonals_prj_l, ['grey'], axis_d / normval,
                           ms=5, zorder = zorder+1)
         #except:
@@ -7036,7 +7042,7 @@ def cells_eigen(amp_desired=[0.5, 1, 5], xlim=[0, 1.1], cell_class=' Ampullary',
     save_visualization(pdf=True)
 
 
-def ampullary_punit(amp_desired = [5,20], xlim=[],add_texts = [0.25,0], cells_plot2=[], RAM=True,scale_val = False,
+def ampullary_punit(eod_metrice = True, amp_desired = [5,20], isi_delta = None,  xlim_p = [0, 1.1], tags_individual = False, xlim=[],add_texts = [0.25,0], cells_plot2=[], RAM=True,scale_val = False,
                     titles=['Low-CV P-unit,', 'High-CV P-unit', 'Ampullary cell,'], peaks_extra=[True, True, True]):#[0, 1.1]
     plot_style()
 
@@ -7077,7 +7083,7 @@ def ampullary_punit(amp_desired = [5,20], xlim=[],add_texts = [0.25,0], cells_pl
         grid1 = big_grid_susept_pics(cells_plot2,  bottom=0.065)
 
     plt_cellbody_singlecell(grid1, frame, amps_desired, save_names, cells_plot2, cell_type_type, xlim=xlim, plus=1,
-                            burst_corr='_burst_corr_individual',RAM=RAM,add_texts = add_texts, titles=titles, peaks_extra=peaks_extra,scale_val = scale_val, )
+                            burst_corr='_burst_corr_individual', eod_metrice = eod_metrice, isi_delta = isi_delta, tags_individual = tags_individual, RAM=RAM,add_texts = add_texts, titles=titles, xlim_p = xlim_p, peaks_extra=peaks_extra,scale_val = scale_val, )
 
     # plt.show()
     save_visualization(pdf=True, individual_tag = cells_plot2[0])