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-% {
-%       Georgios Detorakis,
-%   },
-%             pdftitle=
-% {
-% [Re] A Generalized Linear Integrate-and-Fire Neural Model Produces Diverse
-%         Spiking Behavior
-% },
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-                        Volume \textbf{1} $\vert$
-                        Issue \textbf{1}}
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-% --- Document ----------------------------------------------------------------
-\title{[Re] A Generalized Linear Integrate-and-Fire Neural Model Produces
-            Diverse Spiking Behaviors}
-
-    \usepackage{authblk}
-                        \author[1]{Georgios Detorakis}
-                            \affil[1]{Department of Cognitive Sciences, UC Irvine,
-                            Irvine, CA, USA}
-            
-\date{\vspace{-5mm}
-      \sffamily \small
-      \href{mailto:corresponding-author@mail.com}{gdetorak@uci.edu \\
-      gdetor@protonmail.com}}
-
-
-\setlength\LTleft{0pt}
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-
-
-\begin{document}
-\maketitle
-
-\marginpar{
-  %\hrule
-  \sffamily\small
-  %\vspace{2mm}
-  {\bfseries Editor}\\
-  Name Surname\\
-
-  {\bfseries Reviewers}\\
-        Name Surname\\
-        Name Surname\\
-  
-  {\bfseries Received}  Sep, 1, 2015\\
-  {\bfseries Accepted}  Sep, 1, 2015\\
-  {\bfseries Published} Sep, 1, 2015\\
-
-  {\bfseries Licence}   \href{http://creativecommons.org/licenses/by/4.0/}{CC-BY}
-
-  \begin{flushleft}
-  {\bfseries Competing Interests:}\\
-  The author has declared that no competing interests exist.
-  \end{flushleft}
-
-  \hrule
-  \vspace{3mm}
-
-  \hypersetup{urlcolor=white}
-  
-    \vspace{-1mm}
-  \begin{repobox}
-    \bfseries\normalsize
-      \href{http://github.com/rescience/rescience-submission/article}{\faGithubAlt~Article repository}
-  \end{repobox}
-      \vspace{-1mm}
-  \begin{repobox}
-    \bfseries\normalsize
-      \href{http://github.com/rescience/rescience-submission/code}{\faGithubAlt~Code repository}
-  \end{repobox}
-        \hypersetup{urlcolor=blue}
-}
-
-\begin{rebox}
-\sffamily {\bfseries A reference implementation of}
-\small
-\begin{flushleft}
-\begin{itemize}
-    \item[→] A Generalized Linear Integrate-and-Fire Neural Model Produces
-        Diverse Spiking Behaviors, Stefan Mihalas and Ernst Niebur, Neural
-        Computation 21, 704--718, 2009.
-  \end{itemize}\par
-\end{flushleft}
-\end{rebox}
-
-
-\section{Introduction}\label{introduction}
-
-Integrate-and-fire neurons are being used extensively in the field of 
-neuroscience for modeling spiking behaviors~\cite{dayan:2001}. In this work we
-provide a reference implementation of~\cite{mihalas:2009}, where the authors
-have introduced a generalization of the leaky integrate-and-fire neuron model. 
-The Mihalas-Niebur Neuron (MNN) model is a linear integrate-and-fire neuron
-model capable of expressing a rich spiking behavior based on a set of 
-parameters.
-
-An MNN model expresses tonic and phasic spiking, class $1$ and $2$, spike
-frequency adaptation, accommodation, threshold variability, rebound spike,
-integrator, input bistability, hyperpolarizing spiking and bursting, tonic,
-phasic and rebound bursting, mixed mode, afterpotentials, basal bistability, 
-preferred frequency and spike latency. 
-Due to its simplicity, the MNN model has been used in neuromorphic 
-implementations such as~\cite{folowosele:2011}. 
-
-The model consists of linear differential equations, which describe the membrane
-and threshold potentials and internal currents. All the results provided in 
-\cite{mihalas:2009} have been obtained by using only two internal currents and
-thus we use the exact same number of internal currents in this work. The
-subthreshold dynamics are defined by a set of linear ordinary differential 
-equations, while an instantaneous threshold potential controls when the neuron
-fires an action potential (spike) in a dynamic way. The ability of the MNN 
-model to generate 
-such a diverse spiking behavior is due to the complex update rules. In this 
-work the MNN model has been implemented in Python (version 3.6.1) using 
-Numpy (version 1.13.1) and Matplotlib (version 2.0.2) packages. 
-
-
-
-\section{Methods}\label{methods}
-
-In order to implement the model described in \cite{mihalas:2009}, we discretized
-the dynamical system using the forward Euler integration scheme. The time step
-is fixed to $0.1\, \Rm{ms}$ for all the simulations, and the total simulation
-time $t_f$ varies according to figure 1 of the original paper. Our
-implementation differs from the one in the original paper, since in
-\cite{mihalas:2009}, authors numerically solve equation $3.5$ (algebraic 
-equation) under the constraint imposed by inequality $3.4$ and thus they
-compute the spike times. On the other hand, in this work we directly compute
-numerically the solution of the dynamical system defined by equations $2.1$
-and $2.2$ in \cite{mihalas:2009} (see tables~\ref{table:2} and~\ref{table:3}). 
-
-We provide all equations and parameters of the model in tables as it has been 
-suggested by~\cite{nordlie:2009}. 
-Table~\ref{table:1} provides the summary of the model. Tables~\ref{table:2}
-and~\ref{table:3} give the subthreshold dynamics (differential equations) 
-describing the membrane and the threshold potentials as well as the two internal
-currents and the update rules. The parameters for all the simulations are given
-in table~\ref{table:4}, while the external current intensities and pulse
-duration are provided in table~\ref{table:5}. The parameters in this work are 
-exactly the same used in the original paper (table 1, pg. $711$). We had to 
-infer the time intervals and the total simulation times for the pulses since
-they are not given explicitly in the original paper. Thus, we extracted the
-time intervals from figure $1$ of \cite{mihalas:2009} by visual inspection. 
-The initial conditions are given in table~\ref{table:6}. 
-
-All simulations ran on a Dell OptiPlex $7040$, equipped with a sixth
-generation i$7$ processor, $16\, \Rm{GB}$ of physical memory and running Arch
-Linux (x$86\_64$). The total execution time of all simulations was $2.41$
-seconds and the peak consumed memory was $162\, \Rm{MB}$\footnote{Python memory
-profiler used (\url{https://pypi.python.org/pypi/memory_profiler}).}\@. 
-
-%%
-\begin{table}[!htbp]
-    \centering
-    \begin{tabular}{ll}
-        \thickhline
-        \multicolumn{2}{c}{Model Summary} \\\thickhline
-        \rowcolor{Gray}
-        Populations  & No population -- single neuron model \\\rowcolor{LightGray}
-        Topology     & -- \\ \rowcolor{Gray}
-        Connectivity & -- \\ \rowcolor{LightGray}
-        Neuron Model & Linear Integrate-and-Fire Neuron \\\rowcolor{Gray}
-        Channel Models & Linear, first order ODEs  \\ \rowcolor{LightGray}
-        Synapse Model & -- \\ \rowcolor{Gray}
-        Plasticity & -- \\ \rowcolor{LightGray}
-        Input & Constant current or rectangular pulses \\\rowcolor{Gray}
-        Measurements & Membrane potential, phase plane \\
-        \thickhline
-    \end{tabular}
-    \caption{{\bfseries \sffamily Summary of the model}} 
-    \label{table:1}
-\end{table}
-%%  
-
-%%
-\begin{table}[!htbp]
-    \centering
-    \begin{tabular}{p{3.5cm}ll}
-        \thickhline
-        \multicolumn{2}{c}{Neuron Model} \\\thickhline
-        \rowcolor{Gray}
-        Name  &  Mihalas-Niebur Neuron (MNN) \\ \rowcolor{LightGray}
-        Type  &  Linear Leaky Integrate-and-Fire Neuron  \\ \rowcolor{Gray}
-        Membrane Potential & $
-            \begin{aligned}
-                \frac{\Rm{d}V(t)}{\Rm{d}t} &= \frac{1}{C} \Big(I_e + I_1 + I_2 - G(V(t) -
-                E_L)  \Big)
-            \end{aligned}$ \\ \rowcolor{LightGray}
-        Instantaneous Threshold Potential  & $
-            \begin{aligned}
-                \frac{\Rm{d}\Theta(t)}{\Rm{d}t} &= a(V(t) - E_L) - b(\Theta(t) -
-                \Theta_{\infty}) 
-            \end{aligned} $
-            \\ \rowcolor{Gray}
-        Internal Currents & $
-            \begin{aligned}
-                \frac{\Rm{d}I_{1}(t)}{\Rm{d}t} &= -k_1I_1(t)\\
-                \frac{\Rm{d}I_{2}(t)}{\Rm{d}t} &= -k_2I_2(t) \\
-            \end{aligned} $
-    \end{tabular}
-    \caption{{\bfseries \sffamily Description of the subthreshold dynamics of
-    Mihalas-Niebur neuron model.} $V(t)$ and $\Theta(t)$ are the membrane and 
-    threshold potentials, respectively. $E_L$ and $\Theta_{\infty}$ are the
-    reversal potentials for the membrane and the threshold variables,
-    respectively. $a, b, k_1, k_2$ and $G$ are constant parameters. $I_e$ is
-    the external current applied on the neuron model.} 
-    \label{table:2}
-\end{table}
-%%
-
-\begin{table}[!htbp]
-    \centering
-    \begin{tabular}{cc}
-        \thickhline
-        \multicolumn{2}{c}{Update rules} \\ \thickhline
-        Variable & Rule  \\ \rowcolor{Gray}
-        $V(t)$ & $V_r$  \\ \rowcolor{LightGray}
-        $\Theta(t)$ & $\max\{\Theta_r, \Theta(t) \}$ \\ \rowcolor{Gray}
-        $I_1(t)$ & $R_1 \times I_1(t) + A_1$ \\ \rowcolor{LightGray} 
-        $I_2(t) $ & $R_2 \times I_2(t) + A_2$ \\\thickhline
-    \end{tabular}
-    \caption{{\bfseries \sffamily Update rules.} $V_r$ and $\Theta_r$
-    are the reset values for the membrane and threshold potentials,
-    respectively. $R_1, R_2, A_1$ and $A_2$ are constants.}
-    \label{table:3}
-\end{table}
-%%  
-
-%%
-\begin{table}[!htbp]
-    \centering
-    \begin{tabular}{CCCCC}
-        \thickhline
-        \multicolumn{5}{c}{Model Parameters} \\\thickhline
-           \begin{tabular}[x]{@{}c@{}} Figure \\  \end{tabular}
-         & \begin{tabular}[x]{@{}c@{}} $a$ \\ $(\Rm{s^{-1}})$ \end{tabular}
-         & \begin{tabular}[x]{@{}c@{}} $A_1/C$ \\ $(\Rm{V/s})$ \end{tabular}
-         & \begin{tabular}[x]{@{}c@{}} $A_2/C$ \\ $(\Rm{V/s})$ \end{tabular}& 
-           \begin{tabular}[x]{@{}c@{}} $t_f$ \\ $(\Rm{s})$ \end{tabular} \\
-        \thickhline
-        1A & $0$ & $0$ & $0$ & $0.2$ \\\rowcolor{Gray}
-        1B & $0$ & $0$ & $0$ & $0.5$  \\\rowcolor{LightGray}
-        1C & $5$ & $0$ & $0$ & $0.2$ \\\rowcolor{Gray}
-        1D & $5$ & $0$ & $0$ & $0.5$ \\\rowcolor{LightGray}
-        1E & $5$ & $0$ & $0$ & $1.0$ \\\rowcolor{Gray}
-        1F & $5$ & $0$ & $0$ & $0.4$ \\\rowcolor{LightGray}
-        1G & $5$ & $0$ & $0$ & $1.0$  \\\rowcolor{Gray}
-        1H &  $5$ & $0$ & $0$ & $0.3$ \\\rowcolor{LightGray}
-        1I & $5$  & $0$ & $0$ & $0.4$ \\\rowcolor{Gray}
-        1J & $5$ & $0$ & $0$ & $1.0$ \\\rowcolor{LightGray}
-        1K & $30$ & $0$ & $0$ & $0.4$ \\\rowcolor{Gray}
-        1L & $30$  & $10$ & $-0.6$ & $0.4$ \\\rowcolor{LightGray}
-        1M & $5$ & $10$ & $-0.6$ & $0.5$ \\\rowcolor{Gray}
-        1N & $5$ & $10$ & $-0.6$ & $0.5$ \\\rowcolor{LightGray}
-        1O & $5$ & $10$ & $-0.6$ & $1.0$ \\\rowcolor{Gray}
-        1P & $5$  & $5$ & $-0.3$ & $0.5$ \\\rowcolor{LightGray}
-        1Q & $5$  & $5$ & $-0.3$ & $0.2$ \\\rowcolor{Gray}
-        1R & $0$ & $8$ & $-0.1$ & $0.2$ \\\rowcolor{LightGray}
-        1S & $5$ & $-3$ & $0.5$ & $0.8$ \\\rowcolor{Gray}
-        1T &  $-80$ & $0$ & $0$ & $0.05$ \\
-        \thickhline
-        \multicolumn{5}{l}{Common Parameters} \\\rowcolor{LightGray}
-        \thickhline
-        \multicolumn{5}{p{22.5em}}{
-            $b = 10\, \Rm{s^{-1}}$, $G/C = 50\, \Rm{s^{-1}}$,
-            $k_1 = 200\, \Rm{s^{-1}}$, $k_2 = 20\, \Rm{s^{-1}}$,
-            $\Theta_{\infty} = -0.05\, \Rm{V}$,
-            $R_1 = 0$, $R_2 = 1$, $E_l = -0.07\, \Rm{V}$,
-            $V_r = -0.07\, \Rm{V}$, $\Theta_r = -0.06\, \Rm{V}$.
-        }
-        \\\thickhline
-    \end{tabular}
-    \caption{{\bfseries \sffamily Simulation Parameters}  }
-    \label{table:4}
-\end{table}
-%%
-
-\vspace{-1cm}
-
-%%
-\begin{table}[!htbp]
-    \centering
-    \begin{tabular}{CCp{28.2em}}
-        \thickhline
-        \multicolumn{3}{c}{Model Parameters} \\ \thickhline
-        Figure & Type & $I_e/C (\Rm{V/s})$  \\
-        \thickhline
-        1A & \includegraphics[width=0.1\textwidth]{figs/const.pdf} & $1.5$ \\\rowcolor{Gray}
-        1B & \includegraphics[width=0.1\textwidth]{figs/const.pdf} & $1 + 10^{-6}$ \\\rowcolor{LightGray}
-        1C & \includegraphics[width=0.1\textwidth]{figs/const.pdf} & $2$ \\\rowcolor{Gray}
-        1D &\includegraphics[width=0.1\textwidth]{figs/const.pdf} & $1.5$ \\\rowcolor{LightGray}
-        1E &\includegraphics[width=0.1\textwidth]{figs/pulse.pdf} & 
-              $1.5 (0.1 \Rm{s}),\, 0 (0.5\Rm{s}),\, 0.5 (0.1\Rm{s}),\, 1
-              (0.1\Rm{s}),\, 1.5 (0.1\Rm{s}),\, 0 (0.1 \Rm{s})$
-              \\\rowcolor{Gray}
-        1F &\includegraphics[width=0.1\textwidth]{figs/pulse.pdf} & $1.5 (0.02
-        \Rm{s}),\, 0 (0.18 \Rm{s}),\, -1.5 (0.025 \Rm{s}),\, 0 (0.025
-        \Rm{s}),\, 1.5 (0.025 \Rm{s}),\, 0 (0.125 \Rm{s})$
-        \\\rowcolor{LightGray} 
-        1G & \includegraphics[width=0.1\textwidth]{figs/pulse.pdf} & $0 (0.05
-        \Rm{s}),\, -3.5 (0.756 \Rm{s}),\, 0 (0.194 \Rm{s})$ \\\rowcolor{Gray} 
-        1H & \includegraphics[width=0.1\textwidth]{figs/const.pdf} & $2(1 +
-        10^{-6})$ \\\rowcolor{LightGray}
-        1I & \includegraphics[width=0.1\textwidth]{figs/pulse.pdf} & 
-        \begin{tabular}[x]{@{}c@{}} 
-        $1.5 (0.02 \Rm{s}),\, 0 (0.01 \Rm{s}),\, 1.5 (0.02 \Rm{s}),\,
-            0 (0.25 \Rm{s}),\, 1.5 (0.02 \Rm{s}),\, 0 (0.02 \Rm{s}) $\\
-            $ 1.5 (0.02 \Rm{s}),\, 0 (0.04 \Rm{s})$ 
-        \end{tabular}
-            \\\rowcolor{Gray}
-        1J & \includegraphics[width=0.1\textwidth]{figs/pulse.pdf} & $1.5 (0.1
-        \Rm{s}),\, 1.7 (0.4 \Rm{s}),\, 1.5 (0.1 \Rm{s}),\, 1.7 (0.4 \Rm{s})$
-        \\\rowcolor{LightGray}
-        1K & \includegraphics[width=0.1\textwidth]{figs/const.pdf} & $-1$  \\\rowcolor{Gray}
-        1L & \includegraphics[width=0.1\textwidth]{figs/const.pdf} & $-1$ \\\rowcolor{LightGray}
-        1M & \includegraphics[width=0.1\textwidth]{figs/const.pdf} & $2$ \\\rowcolor{Gray}
-        1N & \includegraphics[width=0.1\textwidth]{figs/const.pdf} & $1.5$  \\\rowcolor{LightGray}
-        1O &\includegraphics[width=0.1\textwidth]{figs/pulse.pdf} & $0 (0.1 
-        \Rm{s}),\, -3.5 (0.5 \Rm{s}),\, 0 (0.4 \Rm{s})$ \\\rowcolor{Gray}
-        1P & \includegraphics[width=0.1\textwidth]{figs/const.pdf} & $2$ \\\rowcolor{LightGray}
-        1Q & \includegraphics[width=0.1\textwidth]{figs/pulse.pdf} & $2 (0.015
-        \Rm{s}),\, 0 (0.185 \Rm{s})$  \\\rowcolor{Gray}
-        1R & \includegraphics[width=0.1\textwidth]{figs/pulse.pdf}& $5 (0.01
-        \Rm{s}),\, 0 (0.09 \Rm{s}),\, 5 (0.01 \Rm{s}),\, 0 (0.09 \Rm{s})$
-        \\\rowcolor{LightGray}
-        1S & \includegraphics[width=0.1\textwidth]{figs/pulse.pdf}&
-        \begin{tabular}[x]{@{}c@{}} 
-            $5 (0.005 \Rm{s}),\, 0 (0.005 \Rm{s}),\, 4 (0.005 \Rm{s}),\, 0 (0.385 \Rm{s}),\,
-            5 (0.005 \Rm{s}),\, 0 (0.045 \Rm{s}) $\\
-            $4 (0.005\Rm{s}),\, 0 (0.345 \Rm{s}) $
-        \end{tabular}
-        \\\rowcolor{Gray}
-        1T & \includegraphics[width=0.1\textwidth]{figs/pulse.pdf} & $8 (0.002 \Rm{s}),\, 0 (0.048 \Rm{s})$ \\
-        \thickhline
-    \end{tabular}
-    \caption{{\bfseries \sffamily External current.} This table provides the
-    external current for each panel in Figure~\ref{fig:1}. There are two types
-    of external currents, constants and pulses. In the case of pulses the
-    duration of each pulse is given in seconds along with its intensity.
-    }
-    \label{table:5}
-\end{table}
-%%
-\vspace{-2cm}
-%%
-\begin{table}[!htbp]
-    \centering
-    \begin{tabular}{cc}
-        \thickhline
-        \multicolumn{2}{c}{Initial Conditions} \\ \thickhline
-        Variable & Initial Value  \\ 
-        \rowcolor{Gray}
-        $V(t)$ & $-0.07\, \Rm{V}$ / $-0.03\, \Rm{V}$ (Figure 1H) \\ \rowcolor{LightGray}
-        $\Theta(t)$ & $-0.05\, \Rm{V}$ / $-0.03\, \Rm{V}$ (Figure 1H) \\ \rowcolor{Gray}
-        $I_1(t)$ & $0.01\, \Rm{V}$ \\ \rowcolor{LightGray} 
-        $I_2(t) $ & $0.001\, \Rm{V}$ \\ \thickhline
-    \end{tabular}
-    \caption{{\bfseries \sffamily Initial conditions.} In all simulations
-    have been used the same initial conditions, except from the one 
-    illustrated in Figure~\ref{fig:1}H.}
-    \label{table:6}
-\end{table}
-%%  
-
-\clearpage
-
-\section{Results}\label{results}
-
-All three figures from the original article have been successfully replicated.
-All the different spiking behaviors of the model are illustrated in 
-Figure~\ref{fig:1}, where the black solid line indicates the membrane potential
-($V(t)$), the red dashed line illustrates the instantaneous threshold potentials
-($\Theta(t)$), and the gray line shows the input to the neuron ($I_e/C$). 
-The $x$-axis scales in all panels are exactly the same as in the original
-paper (indicating the total simulation time ($t_f$), while the $y$-axis scale 
-differs from the one in the original paper. In this work the $y$-axis scale
-is the same same for all the subplots ($[-95, -25]\, \Rm{mV}$), except for
-panels G and O ($[-145, -25]\, \Rm{mV}$).
-
-Figures~\ref{fig:2} and~\ref{fig:3} depict the phase space of the phasic
-spiking ($V(t)$ and $\Theta(t)$) and phasic bursting ($V(t)$,
-$I_1(t)$, and $I_2(t)$). In both figures the blue curves and the black dots 
-indicate the trajectory of the system and spike events, respectively.
-In Figure~\ref{fig:2} the gray arrows show the evolution of the system
-(vector field of the system). Figure~\ref{fig:3} has a different orientation 
-from the original one but both illustrate the same trajectories and spike 
-events of the system. 
-All the figures express the same qualitative behavior as the original
-figures in~\cite{mihalas:2009}.
-
-\begin{figure}[htpb!]
-    \centering
-    \includegraphics[width=0.9\textwidth]{figs/Figure01.pdf}
-    \caption{{\bfseries \sffamily Neural responses of MNN.} Black solid lines
-    indicate the membrane potential ($V(t)$), the red dashed lines show the
-    threshold potentials ($\Theta(t)$), and the gray lines the external
-    currents applied on each case.
-    {\bfseries \sffamily A} tonic spiking,
-    {\bfseries \sffamily B} class $1$,
-    {\bfseries \sffamily C} spike frequency adaptation,
-    {\bfseries \sffamily D} phasic spiking,
-    {\bfseries \sffamily E} accommodation,
-    {\bfseries \sffamily F} threshold variability,
-    {\bfseries \sffamily G} rebound spike,
-    {\bfseries \sffamily H} class $2$,
-    {\bfseries \sffamily I} integrator,
-    {\bfseries \sffamily J} input bistability,
-    {\bfseries \sffamily K} hyperpolarization induced spiking,
-    {\bfseries \sffamily L} hyperpolarization induced bursting,
-    {\bfseries \sffamily M} tonic bursting,
-    {\bfseries \sffamily N} phasic bursting,
-    {\bfseries \sffamily O} rebound burst,
-    {\bfseries \sffamily P} mixed mode,
-    {\bfseries \sffamily Q} afterpotentials,
-    {\bfseries \sffamily R} basal bistability,
-    {\bfseries \sffamily S} preferred frequency,
-    {\bfseries \sffamily T} spike latency.} 
-    \label{fig:1}
-\end{figure}
-
-\begin{figure}[htpb!]
-    \centering
-    \includegraphics[width=0.6\textwidth]{figs/Figure02.pdf}
-    \caption{{\bfseries \sffamily Phase space of phasic spiking.} Blue
-    solid lines indicate the trajectories of the model in the phase spiking
-    behavior (Figure~\ref{fig:1}D). The dashed line corresponds to $V(t) =
-    \Theta(t)$, and the black dots represent spike events. The parameters for 
-    this simulation are the same as in Figure~\ref{fig:1}D.}
-    \label{fig:2}
-\end{figure}
-
-\begin{figure}[htpb!]
-    \centering
-    \includegraphics[width=0.8\textwidth]{figs/Figure03.pdf}
-    \caption{{\bfseries \sffamily Phase space of phasic bursting.} Blue solid
-    lines represent the trajectories of the system and the black dots indicate
-    spiking events. The parameters for this simulation are the same as in
-    Figure~\ref{fig:1}N.}
-    \label{fig:3}
-\end{figure}
-
-\section{Conclusion}\label{conclusion}
-
-All figures in~\textcite{mihalas:2009} have been successfully replicated 
-with high fidelity. Overall, the whole reproducing process was smooth and
-without obscure points since most of the parameters are provided in the
-original article. Only the time intervals for which the external current is
-applied to the model and the initial conditions are not provided explicitly.
-Therefore, we had to extract that information from figure $1$ of the original
-article. To conclude, the
-article~\cite{mihalas:2009} has been successfully reproduced without any
-discrepancy. 
-
-{\sffamily \small
-  \printbibliography[title=References]
-}
-\end{document}