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\journal{Brain Research}
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\begin{document}

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\large

\title{On the precision of neural computation with interaural time
differences in the medial superior olive}
% {is in the order of tens of microseconds}

\author[Poly,Academy,Czech-T]{
 \citen{Zbynek} Bures}
\author[Czech-T,Path,Max]{Petr Marsalek\corref{Correspondent}}
\ead{Marsalek@pks.mpg.de}

\address[Poly]{College of Polytechnics,
 \citen{Tolsteho}~16, 586~01, Jihlava, Czech Republic}

\address[Academy]{Institute of Experimental Medicine, Academy of
 Sciences of the Czech Republic, Videnska~1083, 142~20, Praha~4,
 Czech Republic}

\address[Czech-T]{Czech Technical University in Prague,
 \citen{Zikova}~1903/~4, 166~36, Praha~6, Czech Republic}

% \address[Robotics]{Czech Institute of Informatics, Robotics and
% Cybernetics, Z{i}k{o}v{a}~1903/~4, 166~36, Praha~6, Czech Republic}

\address[Path]{Institute of Pathological Physiology, First Medical
 Faculty, Charles University in Prague, U~Nemocnice~5, 128~53, Praha~2,
 Czech Republic}

\address[Max]{Max Planck Institute for the Physics of Complex Systems,
 \citen{Noethnitzer}\citen{Strasse} 38, 01187 Dresden, Germany}
% \"o, \ss, etc
% $<$Marsalek@pks.mpg.de$>$.}

\cortext[Correspondent]{Corresponding author;
On leave from Prague;
VoIP Telephone +420 910 116629.}

% {VoIP02 +420 910 116629.}
% {VoIP1 +420 910 806 204.}
% {Cell1 +420 777 009 314.}
% {Cell2 +49 170 989 7189.}
% {Fax-CZ +420 224 965 916.}

\begin{abstract}
We study a model of mammalian sound source localization in horizontal
plane. Experiments on small rodents indicate that mammals use broadly
tuned channels of azimuth for localization in horizontal plane. In
mammals this neural computation is implemented by medial superior
olive (MSO) for low frequency sounds. 
%
It has been shown previously that spike timing jitter, coincidence
detection window length, sound frequency, among other input parameters,
influence the output precision, measured by the just noticeable
difference in output of the circuit.
%
It is more difficult, if not impossible, to investigate the effect of
jitter and other parameters mentioned above in electrophysiological
experiment. Therefore we use previously published stochastic model
with spiking neurons. We calculate properties of the model with the
use of analytical methods.
% TO DO: add more text on results.
Predictions of the model we present here have straightforward
applications in testing and designing stimulation protocols used in
cochlear implants.

Abstract must be rewritten.
\comment{
Interaural level difference (ITD) is one of basic binaural clues supporting
the spatial localization of a sound source. The rest of the abstract
will be written later. 
}
\end{abstract}
\begin{keyword}
binaural hearing, coincidence detection, ergodic hypothesis, ideal
observer, interaural time difference, just noticeable difference,
medial superior olive, neuronal arithmetic, sound localization
\end{keyword}
\maketitle

% Check list: \par \par 1 \par 2 \par 3
% \continue % what is this/ Elsevier format, I guess?

\subsection*{Abbreviations and symbols}

% We use following notation, conforming with \citet{Bures2013} and
% \citet{Sanda2012}. Variables are with small letters, constants with
% capitals and Roman indices and with the exception
% my TeX definitions...
% ---
\global\long\def\srom#1{_{{\mathrm #1}}}%
\def\cv{C\srom V}$\cv$,~coefficient of variation;
\def\fsound{f\srom S}$\fsound$,~$f$~sound frequency; 
$F\srom C$,~critical sound frequency value;
$\varphi$,~sound phase;
% Note: sound frequency, in the LSO paper - other symbol;
\def\ILD{\citen{ILD}}\ILD,~interaural level difference;
% \citen{IPD} - interaural phase difference, not used
\def\ITD{\citen{ITD}}\ITD,~interaural time difference;
\citen{ISI},~inter-spike interval; 
\def\JND{\citen{JND}}\JND,~just noticeable difference;
\citen{LSO},~lateral superior olive;
$K\srom C$, $K\srom S$, $A$, $B$, $C$,~proportionality constants; 
$l$,~sound level;
\citen{MSO},~medial superior olive;
$r(V)$, $r\srom V$,~VS,~vector strength;
$R\srom F$,~firing rate;
$\sigma$,~standard deviation;
$T\srom J$,~timing jitter;
$t$,~time;
$T$,~$T\srom{S}$, sound period;
% ---

\section{Introduction}

Mammalian sound localization circuits contain two nuclei in the
auditory brainstem, the medial and the lateral superior olive (MSO and
LSO). Neurons in these nuclei are the first binaural neurons in the
auditory pathway, they are connected to both ears.

This article presents description of information encoding and neural
computation in the MSO obtained mostly with analytical
computations. Using the analytical tools we extend quantitative results
obtained by \citet{Sanda2012} only in simulations and connect them to
\citet{Bures2013} to arrive to unified description of neural circuits
in the superior olive.

\comment{
After a simulation study of the MSO in
\cite{Sanda2012} and an analytical theory of the LSO in
\cite{Bures2013}, we present an analytical and quantitative
calculation of the MSO workings in this paper.
... \citet{Bures2013} ...
\citet{Sanda2012} presented simulation model of the stochastic
computation of the sound azimuth from the \ITD\ in the MSO neural
circuit.
%
In this paper we present some theoretical and analytical calculations
together with the simulations. They are related to the dependence of
the circuit output on sound frequency and sound intensity.
} %\EOC

Due to the physical nature of the binaural sound, the MSO neurons
process spike timing differences in the range of tens of microsecond
($\mu$s).
%
The MSO processes low frequency sounds (in human this is from 20~Hz to
not more than 2~kHz).
%
The LSO processes high frequency sounds (in human this is from 1~kHz
up to 20~kHz).
%
The overlapping region is known to have a sensitivity drop
\cite{Mills1958}.

% Neural coding/ computation
Firing rate, first spike latency and individual spike timings are used
in neural system coding, especially in the auditory pathway. The
highest known spike timing precision has been proven to be utilized in
the mammalian auditory system, \cite{Simmons1989}.
%
Different neural mechanisms are employed in
the two nuclei. 
\continue

\comment{
Even though experimental evidence have changed the
theories of how the MSO computes its output in the decade from
2000 to 2010, for review see \cite{Grothe2010} and 
a quantitative view of this is still missing
\cite{Michelet2012}.
%
The neural computation implemented by the MSO neural circuit is not
entirely described to date.
%
} %\EOC

% The neural circuit in the MSO

Very-raw-paragraph: {\bf In theory} It is reported (citation?)
%
% (interim citations: LSO \cite{Tollin2003}, 
(interim citations: MSO \cite{Grothe2010}...)
%
that workings of the LSO and MSO are largely independent on sound
intensity, so in theory there should not be any dependence.
%
In contrast, since LSO and MSO extract location information with the
use of different physical cues, based on the sound frequency, the
sensitivity of the system to different main sound frequency should be
different.
%
{\bf In practice} After we thoroughly studied mathematical description
of our model of the MSO circuit, we arrived to observations, which we
present here in the following section of results.

First look at the data could enable us to generalize when we speed up,
or slow down the system, which in principle can be done as ten times
up and ten times down, see Table~\ref{tab:speed-up}.

{
MSO is the largest of the nuclei and in human contains approximately
15,500 neurons, LSO contains 5600 cells.
} % \EOC

TO-DO: rewrite this paragraph/ or put it into discussion, at the moment
the text looks repetitive.
%
The range of sound frequencies \cite{Marsalek2004, Grothe2010}
processed in the MSO circuit is limited to the low frequency band. In
human, where audible sound frequencies range from 20~Hz to 20~kHz,
this low frequency band spans from 20~Hz to 1.5~kHz. Moreover, in the
marginal values of this interval, determination of sound azimuth is
below the average taken through all these low frequencies. It is
generally agreed that the main reason, why the workings of the MSO
circuit deteriorate towards higher frequency is lowering of the
synchronization of spike trains in the circuit with the sound source
phase. The synchronization between two corresponding series of point
events can be expressed as a (discrete version of) vector strength,
defined below in equation \eqref{eq:M-VS}.

\section{Results}

We investigate the dependence of the circuit output on sound frequency
and sound intensity.

The degree of synchronization is measured by the vector strength
measure, \eqref{eq:M-VS}.

Figure \ref{fig:FM1V1} shows an example of typical vector strength
lowering towards higher frequencies, as observed in the auditory nerve
(AN). The prevailing majority of neurons in the auditory pathway has a
vector strength spike train statistics sigmoidally dropping towards
higher sound frequencies as it is in this example.
%
In this figure, data recorded by \citet{Joris1996} from the MSO in cat
were fitted to the sigmoidal curve with the general formula of the
Boltzmann function used in \citet{Marsalek2005}.
%
The curve fit of vector strength $r\srom V$ is:
%
\begin{equation}
r\srom V = (1 + \exp(K\srom S \fsound - K\srom C F\srom C))^{-1},
\end{equation}
%
where $K\srom S$ and $K\srom C$ are parameters, proportionality
constants, $\fsound$ is sound frequency and $F\srom C$ is another
parameter, critical half frequency.

Figure \ref{fig:FM2V1} shows the best \JND\ obtained with the basic
parameter set in dependence on the sound frequency.
%
The quadratic curve fit of the \JND\ denoted $\Delta t\srom{\JND}$ is:
%
\begin{equation}
\Delta t\srom{\JND} = A (f\srom S - F\srom C)^2 + B,
\end{equation}
%
where $A$ and $B$ are fitted constants and the other parameters and
variables are as above. See also \citet{Zwislocki1956}.

Figure \ref{fig:FM3V1} shows existence of both a critical and an
optimal values of timing jitter in respect to the attaining of the
\JND\ and output firing rate. Simulations show that with lowering
timing jitter the circuit output is virtually more and more precise.
%
The two curve fits are:

1. fit of exponential function to simulations:
\begin{equation}
\Delta t\srom{\JND}= \exp(A(\tau\srom{J}-B)) - C,
\end{equation}
%
where $A~=~1.9$, $B~=~1.25$ and $C~=~0.2$ are fitted parameters.

2. Another fit, together with noise, to a quadratic function:
%
\begin{equation}
\Delta t\srom{\JND} = A(\tau\srom{J}-B)^2 + C,
\end{equation}
%
where $A~=~2.5$, $B~=~1$, $C~=~1$, \textit{this must be explained -
  that this fit is after normalization!}.
%
\par\indent
%
Logarithm of the \JND\ lowers with a curve close to the square root of
the jitter. This trend ends up at the critical value of jitter:
$\tau\srom{J}\approx~0.5~ms$. Beyond that point towards the lower
jitter values, the neural circuit cannot function properly, as a too
low jitter prevents the interaction of spikes from the left and right
side within the coincidence detection mechanism. The other (optimal)
jitter value notable in this Figure~\ref{fig:FM3V1} is firing rate
in dependency on the jitter values. 
%
\comment{
This is an analytical dependency obtained in \cite{Salinas2000} for a
perfect integrator model with several inputs. The mechanism studied
thereof is close to the MSO neural mechanism studied here.}
%
The firing rate changes in dependency on the input spike timing
variability of {\it partially correlated} definition?/ description?
input spike trains.

Figure \ref{fig:FM4V1} shows the calibration curve. The rising slope
of this curve is used as a read{out} function yielding the firing rate
in dependency on the \ITD\, which in turn signals the sound azimuth to
the next nuclei up to the auditory pathway.

\begin{table}
\begin{centering}
\begin{tabular}{|l |l | r| c | c |}
\hline 
Parameter & Symbol & Units & Typical Value & Ranges\tabularnewline
\hline 
\hline 
Jitter & $T_{J}=\sigma$ & ms & 1 & 0.125 - 8 \tabularnewline
\hline 
Window & $w_{CD}$ & ms & 0.6 & 0.15 - 1.5 \tabularnewline
\hline 
Sound Frequency & $f$ & Hz & 200 & 40-1600 \tabularnewline
\hline 
 & & & & \tabularnewline
\hline 
\end{tabular}
\par\end{centering}

\caption{\label{tab:parameters}
The basic set of parameters. 
%should be here, in analogy to the tables in \citet{Sanda2012}.
}
\end{table}

TO-DO - This Table 1 should be extended: more parameters and range of
parameters...

% %%%
\begin{table}
\begin{centering}
\begin{tabular}{|l |l | r|| c | c | c | c |}
\hline 
% octaves
Parameter & Symbol & Units &
 4$\times$ Slower & Values (man)&
 4$\times$ Faster & 8$\times$ Faster \cr\hline\hline 
Sound frequency & $f$ & Hz & 35 & 140 & 560 & 1120 \cr\hline
Window size & $w\srom{CD}$ & $\mu$s & 2400 & 600 & 150 & 75 \cr\hline 
Timing Jitter & $T\srom{J}$ & ms & 4 & 1 & 0.25 & 0.125 \cr\hline 
Predicted \JND\ & $T$ & $\mu$s &
 40 & 10 & 2.5 & 1.25 \cr\hline
Predicted detection time$^{*}$ & $T$ & ms &
 2,600 & 650 & 162.5 & 81.25 \cr\hline
\end{tabular}
\par\end{centering}
%
\caption{\label{tab:speed-up}
This Table is direct scaling of parameters used in \citet{Sanda2012}.}
Predicted values are: 1) just noticeable difference (\JND) of the \ITD\
% expressed as a ratio of the \ITD\ over
2) the time of observation by an ideal observer reading the information
from one neuron.
\label{tab:frequency}
\end{table}
% %%%

\section{Discussion}

\subsection{Things to do}

\comment{Outline:\begin{enumerate}
% \hline
OK \item Content and results of this paper = analytical investigations of
our model we explored before mostly by simulation.
\item Relation to data
OK \item Relation of MSO and LSO
Discussion \item alternative theories of LSO/ MSO functioning
\end{enumerate}} %\EOC

\comment{Simulations to support/ correct the theoretical results.}

First: Reproduce the curve in panel with sound frequency on x-axis and
\JND\ on the y-axis in Fig. \ref{fig:FM2V1}. %{fig:FM-J-N-D}.

Second: Present the curve from the right panel of Fig.4 from Sanda P.,
Jitter Effect on the Performance..., 
% E-J-B-I - 1:1, 2011.
as part of Fig. \ref{fig:FM3V1}. %{fig:FM-J-I-T-T}.

Some other tentative questions: What is the highest slope of the \ITD\
interpolation curve, such that it gives the resolution of 4$^\circ$
(angular degrees)?

(This we will do later: We also discuss the relation of the MSO neural
circuit to its neighboring nucleus, the LSO, and its output.)

\comment{To cite: exotic media, like \citen{Scholarpedia}, or
  \citen{ArXive}, see theoretical work in \citen{ArXive} 
  \citen{Mlynarski2014}
} % \EOC 
Cite: \cite{Mlynarski2014}...

% We should further discuss \cite{Sanda2011}, etc, etc...

%%% End Discussion / Beginning Figures

%%%
\comment{
\begin{figure}[ht]
\begin{centering}
\includegraphics[scale=0.125]{figs/GENERIC}
\par\end{centering}
\noindent
\caption{
\textbf{Caption title.}
%
Figure description
%
}
\label{fig:FMSCHEM} 
\end{figure}
} % end comment
%%%

\begin{figure}[ht]
\begin{centering}
% [width=\textwidth]
% \includegraphics[width=13cm]{figs/FM1V1}
\includegraphics[scale=0.8]{figs/FM1V1}
\par\end{centering}
\noindent
\caption{ 
%
\textbf{Vector strength of auditory nerve spike trains
in dependence on sound frequency.}
%
X-axis shows fundamental sound frequency in~Hz in a logarithmic scale
and y-axis shows the vector strength.
%
Even though is some nuclei up the auditory pathway the synchronization
can be maintained towards higher frequencies than shown here, the
decrease of the vector strength towards higher frequencies is a
general property of all neurons in the auditory pathway.}
% figure 1
\label{fig:FM1V1} 
\end{figure}

\comment{
This schema shows the anatomical wiring of the mammalian brainstem
circuit together with the equivalent circuit used in the model.
%
Without the loss of generality, for the sake of simplicity, only one
half of the circuit is shown. Ipsi-lateral and contra-lateral sides
are shown as a left and right sides, respectively
}
%

\begin{figure}[ht]
\begin{centering}
\includegraphics[scale=0.8]{figs/FM2V1}
\par\end{centering}
\noindent
\caption{
\textbf{The shortest \ITD\ detected in the dependence on sound frequency.}
%
X-axis shows sound frequency in~Hz in a logarithmic scale and y-axis
the shortest \JND\ in $\mu$s. This is a theoretical prediction based on
the analytical model and basic parameter set used in previous
simulations.
% figure 2
}
\label{fig:FM2V1} 
\end{figure}

\begin{figure}[ht]
\begin{centering}
\includegraphics[scale=0.8]{figs/FM3V1}
\par\end{centering}
\noindent
\caption{
%
\textbf{The \JND\ values in basic parameter set in dependence on the
jitter magnitude.}
%
This plot in semi-logarithmic y-scale shows just noticeable difference
of interaural time difference depending on variation of the spike
timing jitter. Jagged line: simulated data,
% from \cite{Sanda2012},
solid line: an exponential fit to the simulations under the assumption
of arbitrary time precision in the model circuit, line with circles: a
quadratic function estimate of spike timing precision in a system with
addition of noise.
% figure 3
}
\label{fig:FM3V1}
\end{figure}

\begin{figure}[ht]
\begin{centering}
\includegraphics[scale=0.8]{figs/FM4V1}
\par\end{centering}
\noindent
\caption{
\textbf{Gaussian fit of the \ITD\ to firing rate slope calibration curve.}
%
X-axis shows \ITD\ in~ms and y-axis shows corresponding firing rate in
action potentials per second. Note that the curve peak is offset from
the origin of coordinates at $t\srom{\ITD}=0$.
%
}
\label{fig:FM4V1} 
\end{figure}

\section{Experimental Procedures}

\subsection{Preliminaries}

Our procedures are mostly in using computational models, experimental
data and statistical methods. Here we list several assumptions used in
the paper.

\begin{inparaenum}[(1)]
\item Single MSO neuron constants, see \cite{Toth2015}.
\item Synaptic machinery, see \cite{Toth2015}.
\item Ergodic assumption see \cite{Sanda2012}.
\end{inparaenum}

% keywords: Vector strength, ideal observer, phenomenological spike,
% just noticeable difference

\subsection{Vector strength}

The vector strength is defined as it is used since
\citet{Goldberg1969}.
%
Let us have sample (spike) phases 
%
$\varphi_i$, $i=1, 2, \dots, N$ 
%
relative to phases of a given input master periodic function, which
does not enter the formula. (Such function can be for example sound
stimulus eliciting the spike train as a response.)
%
(Discrete sum) vector strength of sample
%
\( \varphi_1, \dots, \varphi_N \) is defined as
%
\begin{equation}
r\srom V(\varphi_i) = \frac{1}{N}
\sqrt{\left( \sum_{i=1}^N \cos\varphi_i \right)^2
+ \left( \sum_{i=1}^N\sin\varphi_i \right)^2}.
\label{eq:M-VS}
\end{equation}

\subsection{\label{Meth_JND}Just noticeable differences of \ITD}

This subsection of Methods should be analogous to the corresponding
section in \citet{Bures2013}...

\comment{
A higher variability of firing leads to a lower precision of the rate
code. Intuitively, if a repeated presentation of the same stimulus
evokes each time a different spike count, then to distinguish between
two different stimuli, the associated spike count change must be larger
than the spike count variability. Mathematically, the current work
determines the \JND\ of \ILD\
(in other words, the precision of \ILD\ coding)
using theoretical tools. 
% derived in \citen{Green}1966, \citen{Teich}1979, \citen{Colburn}2003).
We will ask whether it is possible to distinguish between two random
processes with rates $L_{1}$ and $L_{2}$, $L_{2}>L_{1}$. If we
count events in a given counting window, we get counts $n_{1}$ and
$n_{2}$. The probability that the observer obtains a result that
$L_{2}>L_{1}$ equals to the probability that $n_{2}>n_{1}$. Let's
assume that the random variables $n_{i}$, $i=1,2$, have probability
distribution $p(n_{i})$ with means $\mu_{i}$ and roughly equal standard
deviation $\sigma$. A detection distance is then defined as 
\begin{equation}
d'=\frac{\mu_{2}-\mu_{1}}{\sigma}\label{R_SDT1}
\end{equation}
This definition expresses the fact that the larger the variance of
the spike count, the worse the detection capability.

In psychophysics, a threshold value is commonly defined as that value
for which the percentage of correct answers equals 75. In our case,
the examined value is the just-noticeable change of firing rate,
$\Delta L=L_{2}-L_{1}$.
%
Assuming that both $p(n_{1})$ and $p(n_{2})$ are Gaussian, the 75\%
probability of $n_{2}-n_{1}>0$ corresponds to $d'=1$. To obtain
the \JND\ of firing rate, we scale the detection distance with $\Delta L$
and put $\delta'=d'/\Delta L$. Then, the \JND\ of firing rate is 

\begin{equation}
\Delta L_{\text{\JND}}=1/\delta'=
\frac{L_{2}-L_{1}}{\mu_{2}-\mu_{1}}\sigma\label{R_SDT2}
\end{equation}
} %end - comment

\subsection{Model of the MSO neural circuit}

To do: Describe model and calculations.

% \nocite us/ mutual citations:
% Author(s): \citen{Koutsou}; \citen{Christodoulou}; \citen{Bugmann};
% and others
% Source: NEURAL COMPUTATION, Volume: 24, Issue: 9, Pages: 2318-2345 

\subsection*{Acknowledgments}

\comment{Date is now: \today.
These were the last projects in 2013/ 2014,
Supported by projects M00176 ``Co-operation in biomedicine and
electronics'' and ``Support of individual careers of prospective
academic workers'' to Z.B. at the College of Polytechnics Jihlava
and by a graduate students' research program of Charles University
% S{V}V - 2014 - 260033 to P.M.
S{V}V - 2015 - 260157,
etc...}

Supported by projects X{Y}Z to Z.B. at the College of Polytechnics
Jihlava and by a graduate students' research program of Charles
University
% S{V}V - 2014 - 260033 to P.M.
S{V}V - 2017..2019 - no. 260x{x}x,
\citen{PRVOUK} ?,
and by internal grant of the Max-Planck Society to P.M.

\subsection*{Notes}
%
% by graduate research program no. 266509/ 2013 to P.M. at the Charles
% University 
% thanks to X{Y}Z for English language copy-editing.
%%% \newpage
%%% \nocite{*} % = all records in the ../sound_local.bib file are OK
%\nocite{XX}

To Do: Add citations to discussion:

Cite \cite{Franken2014}, this is an experimental description of MSO
workings in line with our concepts and coincidence detection theories.

Cite \cite{Silver2010}, because this is a prospect of neuronal
arithmetic.

To do: check, whether both \citet{Smith1991} and \citet{Smith1993} are
relevant.

The ergodic paper of \citet{Toth17XX} goes to Discussion.

Another MSO model, already in 2005 is: \citet{Zhou2005}.

Cochlear implants and MSO: \cite{Kerber2012}.

Check \citet{Wu1992}: WHY DO WE CITE THIS?

Here are the three LSO experimental papers, which have detailed
methodology applicable to overlap of sound frequency ranges with the MSO
\cite{Joris1995}; % = LSO I
\cite{Joris1996}; % = LSO II
\cite{Joris1998_envelope}. % = LSO III

\citet{Jeffress1962} goes instead of Jeffress, 1948.

% below are several no-cite items/ alphabetical order

(No)Cite: \citet{Bures2012} is neuronal arithmetic in \citen{Biol.}
\citen{Cybern.}

(No)Cite: \cite{Bures2013} is ``dual'' paper in Brain \citen{Res.} 

Do we still need to cite this review: \cite{Grothe2010}?...

Thee are the classics...: \cite{Goldberg1969}.

Cochlear delays, very important: \citet{Joris2006}.

Yet another neuronal arithmetic: \cite{Koutsou2012}.

This is about other dimension; if we cite this, then we must discuss all
the three, \citet{Kopco2012}. hall we do that?

Jitter and variability after 15 years: this we need..: \cite{Kostal2010}.

\nocite{Krips2009}

\nocite{Laback2008}

This is a chronological record of the neuronal arithmetic ideas,
starting from:
\citet{Marsalek2000},
through
\citet{Marsalek2001}
some more detailed account o the diversity of cell functions and shapes:
\citet{Reed2002},
through
\citet{Marsalek2004} 
to the
\citet{Marsalek2005_Kofranek};
watch the years ...

Remains to be cited: no-cites of \citet{Smith1993} and:
\citet{Szalisznyo2004}.

% http://en.wikipedia.org/wiki/Superior_olivary_complex
\comment{
  % \citen{Kulesza}, R. J., Jr. (2007). "Cyto{-}architecture of the
  % human superior olivary complex: medial and lateral superior olive."
  % Hear Res 225(1-2): 80-90.
} % \EOC
 
\small
\newpage

\bibliographystyle{elsarticle-harv}
%\bibliographystyle{abbrv} % = numbered
\bibliography{sound_local}
%\printbibliography %{sound_local}

\end{document}

\comment{
% log: 2013, ...
% 2015-09-10, 2015-10-27, 
}
