little fixes and removed some comments

Makefile

@@ -6,7 +6,7 @@ TEXFILE=$(TEXBASE).tex
 PDFFILE=$(TEXBASE).pdf
 TXTFILE=$(TEXBASE).txt
 
-REVISION=249fe01cabd4632dee366e26991fe32f4c1286df
+REVISION=493ed7b1e730a76adb6522831a34873d6fb525b5
 
 PDFFIGURES=$(shell sed -n -e '/^[^%].*includegraphics/{s/^.*includegraphics.*{\(.*\)}/\1.pdf/;p}' $(TEXFILE))
 

manuscript.tex

@@ -285,7 +285,7 @@ To examine the role of cell-type specific ionic current environments on the impa
 \end{figure}
 
 \subsection*{Variety of model neurons}
-Neuronal firing is heterogenous across the CNS and a set of neuronal models with heterogenous firing due to different ionic currents is desirable to reflect this heterogeneity. The set of single-compartment, conductance-based neuronal models used here has considerable diversity as evident in the variability seen across neuronal models both in spike trains and their fI curves (\Cref{fig:diversity_in_firing}). The models chosen all fire tonically and do not exhibit bursting. See methods for details and naming of the models. Some models, such as Cb stellate and RS inhibitory models, display type I firing whereas others such as Cb stellate \(\Delta\)\Kv and STN models have type II firing. Type I firing is characterized by continuous fI curves (i.e. firing rate increases from 0 in a continuous fashion) whereas type II firing is characterized by a discontinuity in the fI curve (i.e. a jump occurs from no firing to firing at a certain frequency). The other models used here lie on a continuum between these prototypical firing classifications. Most neuronal models exhibit hysteresis with ascending and descending ramps eliciting spikes at different current thresholds, however the STN +\Kv, STN \(\Delta\)\Kv, and Cb stellate \(\Delta\)\Kv models have large hysteresis (\Cref{fig:diversity_in_firing}, \Cref{fig:ramp_firing}).  \notejb{No bifurctions, but mention different dynamics!} \notenk{Bifurcations removed and new sentence like this?} Different underlying dynamics generate these different firing types and hysteresis \cite{ERMENTROUT2002, ermentrout_type_1996, Izhikevich2006}.
+Neuronal firing is heterogenous across the CNS and a set of neuronal models with heterogenous firing due to different ionic currents is desirable to reflect this heterogeneity. The set of single-compartment, conductance-based neuronal models used here has considerable diversity as evident in the variability seen across neuronal models both in spike trains and their fI curves (\Cref{fig:diversity_in_firing}). The models chosen all fire tonically and do not exhibit bursting. See methods for details and naming of the models. Some models, such as Cb stellate and RS inhibitory models, display type I firing whereas others such as Cb stellate \(\Delta\)\Kv and STN models have type II firing. Type I firing is characterized by continuous fI curves (i.e. firing rate increases from 0 in a continuous fashion) whereas type II firing is characterized by a discontinuity in the fI curve (i.e. a jump occurs from no firing to firing at a certain frequency). The other models used here lie on a continuum between these prototypical firing classifications. Most neuronal models exhibit hysteresis with ascending and descending ramps eliciting spikes at different current thresholds, however the STN +\Kv, STN \(\Delta\)\Kv, and Cb stellate \(\Delta\)\Kv models have large hysteresis (\Cref{fig:diversity_in_firing}, \Cref{fig:ramp_firing}). \notejb{No bifurcations: How about this?} Different types of the underlying voltage and gating dynamics are known to generate these different firing types and hysteresis \cite{ERMENTROUT2002, ermentrout_type_1996, Izhikevich2006}.
 
 \subsection*{Characterization of Neuronal Firing Properties}
 \begin{figure}[tp]
@@ -293,14 +293,14 @@ Neuronal firing is heterogenous across the CNS and a set of neuronal models with
         \includegraphics[width=0.5\linewidth]{Figures/firing_characterization.pdf}
 	\includegraphics[width=0.5\linewidth]{Figures/firing_characterization_arrows.pdf}
 	\\\notenk{Re-work legend to be in line with new figure}
-        \\\notejb{Add arrows to indicate change, make red fI curve in the bottom left smaller. Alternative: legend below the panel.} \notenk{Both done, which one do you like better? I think I like the legend one better, but I'm not sure what the best legend label for the red one I've labelled ``Altered'' is.}
+        \\\notenk{Both done, which one do you like better? I think I like the legend one better, but I'm not sure what the best legend label for the red one I've labelled ``Altered'' is.}\notejb{I really like the arrows! Having two arrows is great. I Would make the lower one always horizontal to make it indicative for the rheobase. If space gets too narrow for this shift the fI curves further appart.}
         \linespread{1.}\selectfont
         \caption[]{Characterization of firing with AUC and rheobase. (A) The area under the curve (AUC) of the repetitive firing frequency-current (fI) curve. (B) 
 Changes in firing as characterized by \(\Delta\)AUC and \(\Delta\)rheobase occupy 4 quadrants separated by no changes in AUC and rheobase. Representative schematic fI curves in blue with respect to a reference fI curve (black) depict the general changes associated with each quadrant.}
         \label{fig:firing_characterization}
 \end{figure}
 
-Neuronal firing is a complex phenomenon and a quantification of firing properties is required for comparisons across cell types and between conditions. Here we focus on two aspects of firing: rheobase, the smallest injected current at which the cell fires an action potential, and the shape of the frequency-current (fI) curve as quantified by the area under the curve (AUC) for a fixed range of input currents above rheobase (\Cref{fig:firing_characterization}A). \notejb{this way we make the AUC independent from changes in rheobase} \notenk{How about ``This enables a AUC measurement independent from rheobase.''}The characterization of firing by rheobase and AUC allows to characterize both a neuron's excitability in the sub-threshold regime (rheobase) and periodic firing in the super-threshold regime (AUC). Note that AUC is essentially quantifying the slope of a neuron's fI curve.
+Neuronal firing is a complex phenomenon and a quantification of firing properties is required for comparisons across cell types and between conditions. Here we focus on two aspects of firing: rheobase, the smallest injected current at which the cell fires an action potential, and the shape of the frequency-current (fI) curve as quantified by the area under the curve (AUC) for a fixed range of input currents above rheobase (\Cref{fig:firing_characterization}A). \notenk{This enables a AUC measurement independent from rheobase.}\notejb{I added a few words to the next sentence. Would this be enough or should we make it more explicit by an extra sentence als Nils suggests it?} The characterization of firing by rheobase and AUC allows to characterize both a neuron's excitability in the sub-threshold regime (rheobase) and periodic firing in the super-threshold regime (AUC) by two independent measures. Note that AUC is essentially quantifying the slope of a neuron's fI curve.
 
 Using these two measures we quantify the effects a changed property of an ionic current has on neural firing by the differences in both rheobase, \drheo, and in AUC, \(\Delta\)AUC, relative to the wild type neuron. \(\Delta\)AUC is in addition normalized to the AUC if the wild type neuron, see Eq.~\eqref{eqn:AUC_contrast}. Each fI curve resulting from an altered ionic current is a point in a two-dimensional coordinate system spanned by \drheo and \ndAUC (\Cref{fig:firing_characterizaton}B). An fI curve similar to the one of the wild type neuron is marked by a point close to the origin. In the upper left quadrant, fI curves become steeper (positive difference of AUC values: \(+\Delta\)AUC) and are shifted to lower rheobases (negative difference of rheobases: \(-\)\drheo), unambigously indicating an increased firing that clearly might be classified as a GOF of neuronal firing. The opposite happens in the bottom right quadrant where the slope of fI curves decreases (\(-\Delta\)AUC) and the rheobase is shifted to higher currents (\(+\)\drheo), indicating a decreased, LOF firing. In the lower left (\(-\Delta\)AUC and \(-\)\drheo) and upper right (\(+\Delta\)AUC and \(+\)\drheo) quadrants, the effects on firing are less clear-cut, because the changes in rheobase and AUC have opposite effects on neuronal firing. Changes in a neuron's fI curves in these two quadrants cannot uniquely be described as a gain or loss of excitability. In these cases it depends on the regime the neuron is operating in. If it is in its excitable regime and only occasionaly generates an action potential, then the effect on the rheobase matters more. If it is firing periodically with high rates, then the change in AUC might be more relevant.
 
@@ -313,10 +313,6 @@ Changes in gating half activation potential \(V_{1/2}\) and slope factor \(k\) a
 
 Qualitative differences can be found, for example, when increasing the maximal conductance of the delayed rectifier (\Cref{fig:AUC_correlation}~G,H,I). In some model neurons this increased AUC (\( \text{Kendall} \ \tau \approx +1\)), whereas in others AUC is decreased (\( \text{Kendall} \ \tau \approx -1\)). In the STN +\Kv model, AUC depends in a non-linear way on the maximal conductance of the delayed rectifier, resulting in an \( \text{Kendall} \ \tau \) close to zero. Even more dramatic qualitative differences between models result from shifts of the activation curve of the delayed rectifier, as discussed already above (\Cref{fig:AUC_correlation}~A,B,C). Some model neurons do almost not depend on changes in K-current half activation \(V_{1/2}\) or show strongly non-linear dependencies, both resulting in \( \text{Kendall} \ \tau\) close to zero. Many model neurons show strongly negative correlations, and a few show positive correlations with shifting the activation curve of the delayed rectifier.
 
-\notejb{the slope factor has a name (``slope factor''), but \(V_{1/2}\) not. How is this called, ``midpoint potential''? ``half activation potential/voltage''}\notenk{Changed this to half activation or half inactivation when only talking about activation or inactivation and to  half maximal potential when referring to both activation and inactivation.} 
-
-\notejb{``maximum conductance''} \notenk{fixed this in results section}
-
 
 \begin{figure}[tp]
         \centering
@@ -336,7 +332,7 @@ Qualitative differences can be found, for example, when increasing the maximal c
 
 \notejb{Add more detailed step-by-step description what we did here, like for AUC.}\notenk{started doing this}
 
-Changes in gating half activation potential \(V_{1/2}\) and slope factor \(k\) as well as the maximum conductance \(g\) affect rheobase (\Cref{fig:rheobase_correlation}), however in contrast to AUC qualitatively consistent effects on rheobase across models are observed. Increasing, for example, the maximal conductance of the leak current in the Cb stellate model increases the rheobase (\Cref{fig:rheobase_correlation}~G). When these changes are plotted against the change in maximal conductance a monontonically increasing relationship is evident (thick teal line in \Cref{fig:AUC_correlation}~H). This monotonically increasing relationship is evident in all models (\( \text{Kendall} \ \tau \approx +1\)), but with different slopes (thin lines in \Cref{fig:rheobase_correlation}~H). Similarily, positive correlations are consistently found across models for maximal conductances of delayed rectifier K, \Kv, and A type currents, whereas the maximal conductance of the sodium current consistently is associated with negative correlations (\( \text{Kendall} \ \tau \approx +1\); \Cref{fig:rheobase_correlation}~I). 
+Changes in gating half activation potential \(V_{1/2}\) and slope factor \(k\) as well as the maximum conductance \(g\) affect rheobase (\Cref{fig:rheobase_correlation}), however, in contrast to AUC, qualitatively consistent effects on rheobase across models are observed. Increasing, for example, the maximal conductance of the leak current in the Cb stellate model increases the rheobase (\Cref{fig:rheobase_correlation}~G). When these changes are plotted against the change in maximal conductance a monontonically increasing relationship is evident (thick teal line in \Cref{fig:AUC_correlation}~H). This monotonically increasing relationship is evident in all models (\( \text{Kendall} \ \tau \approx +1\)), but with different slopes (thin lines in \Cref{fig:rheobase_correlation}~H). Similarily, positive correlations are consistently found across models for maximal conductances of delayed rectifier K, \Kv, and A type currents, whereas the maximal conductance of the sodium current consistently is associated with negative correlations (\( \text{Kendall} \ \tau \approx -1\); \Cref{fig:rheobase_correlation}~I), i.e. rheobase is decreased with increasing maximum conductance in all models.
 
 Although changes in half maximal potential \(V_{1/2}\) and slope factor \(k\) generally correlate with rheobase similarly across model there are some exceptions. Changing the slope factor of Na-current inactivation, \Kv-current inactivation, and A-current activation affect rheobase both with positive and negative correlations in different models (\Cref{fig:rheobase_correlation}~F). Departures from monotonic relationships also occur in some models as a result of K-current activation  \(V_{1/2}\) and slope factor \(k\), \Kv-current inactivation slope factor \(k\), and A-current activation slope factor \(k\) in some models. Thus, identical changes in current gating properties such as the half maximal potential \(V_{1/2}\) or slope factor \(k\) can have differing effects on firing depending on the model in which they occur. \notejb{This we just have said...}