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\@writefile{toc}{\contentsline {section}{\numberline {1}Zusammenfassung}{4}{section.1}}
\@writefile{toc}{\contentsline {section}{\numberline {2}Abstract}{4}{section.2}}
\@writefile{toc}{\contentsline {section}{\numberline {3}Introduction}{4}{section.3}}
\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces  Example behavior of a p-unit with a high baseline firing rate. Baseline Firing: A 100\tmspace  +\thinmuskip {.1667em}ms voltage trace of the recording with spikes marked by the black lines. ISI-histogram: The histogram of the ISI with the x-axis in EOD periods, showing the phase locking of the firing. Serial Correlation: The serial correlation of the ISI showing a negative correlation for lags one and two. Step Response: The response of the p-unit to a step increase in EOD amplitude. In {\color  {red}(TODO: color)} the averaged frequency over 10 trials and in {\color  {red}(TODO: color)} smoothed with an running average with a window of 10\tmspace  +\thinmuskip {.1667em}ms. The p-unit strongly reacts to the onset of the stimulus but very quickly adapts to the new stimulus and then shows a steady state response. FI-Curve: The fi-curve visualizes the onset and steady-state response of the neuron for different step sizes (contrasts). In {\color  {red}(TODO: color)} the detected onset responses and the fitted Boltzmann, in {\color  {red}(TODO: color)} the detected steady-state response and the linear fit.}}{4}{figure.1}}
\newlabel{fig:p_unit_example}{{1}{4}{Example behavior of a p-unit with a high baseline firing rate. Baseline Firing: A 100\,ms voltage trace of the recording with spikes marked by the black lines. ISI-histogram: The histogram of the ISI with the x-axis in EOD periods, showing the phase locking of the firing. Serial Correlation: The serial correlation of the ISI showing a negative correlation for lags one and two. Step Response: The response of the p-unit to a step increase in EOD amplitude. In \todo {color} the averaged frequency over 10 trials and in \todo {color} smoothed with an running average with a window of 10\,ms. The p-unit strongly reacts to the onset of the stimulus but very quickly adapts to the new stimulus and then shows a steady state response. FI-Curve: The fi-curve visualizes the onset and steady-state response of the neuron for different step sizes (contrasts). In \todo {color} the detected onset responses and the fitted Boltzmann, in \todo {color} the detected steady-state response and the linear fit}{figure.1}{}}
\citation{walz2013Phd}
\citation{walz2014static}
\citation{todd1999identification}
\@writefile{toc}{\contentsline {section}{\numberline {4}Materials and Methods}{5}{section.4}}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.1}Cell recordings}{5}{subsection.4.1}}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.2}Stimulus Protocols}{6}{subsection.4.2}}
\newlabel{eq:am_generation}{{1}{6}{Stimulus Protocols}{equation.4.1}{}}
\newlabel{fig:stim_examples}{{2}{6}{Example of the stimulus construction. At the top a recording of the fish's EOD. In the middle: EOD recording multiplied with the AM, with a step from 0 to a contrast of 30\,\% between 0 and 50\,ms (marked in \todo {color}). At the bottom the resulting stimulus trace when the AM is added to the EOD. \todo {Umformulieren}}{figure.2}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces  Example of the stimulus construction. At the top a recording of the fish's EOD. In the middle: EOD recording multiplied with the AM, with a step from 0 to a contrast of 30\tmspace  +\thinmuskip {.1667em}\% between 0 and 50\tmspace  +\thinmuskip {.1667em}ms (marked in {\color  {red}(TODO: color)}). At the bottom the resulting stimulus trace when the AM is added to the EOD. {\color  {red}(TODO: Umformulieren)}}}{6}{figure.2}}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.3}Cell Characteristics}{6}{subsection.4.3}}
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\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces  {\color  {red}(TODO: place right in text)}On the left: The averaged response of a cell to a step in EOD amplitude. The beginning (at 0\tmspace  +\thinmuskip {.1667em}s) and end (at 1\tmspace  +\thinmuskip {.1667em}s) of the stimulus are marked by the gray lines. The detected values for the onset ($f_0$) and steady-state ($f_{inf}$) response are marked in {\color  {red}(TODO: color)}. $f_0$ is detected as the highest deviation from the mean frequency before the stimulus while $f_{inf}$ is the average frequency in the 0.1\tmspace  +\thinmuskip {.1667em}s time window, 25\tmspace  +\thinmuskip {.1667em}ms before the end of the stimulus. On the right: The fi-curve visualizes the onset and steady-state response of the neuron for different stimuli contrasts. In {\color  {red}(TODO: color)} the detected onset responses and the fitted Boltzmann, in {\color  {red}(TODO: color)} the detected steady-state response and the linear fit.}}{8}{figure.3}}
\newlabel{fig:f_point_detection}{{3}{8}{\todo {place right in text}On the left: The averaged response of a cell to a step in EOD amplitude. The beginning (at 0\,s) and end (at 1\,s) of the stimulus are marked by the gray lines. The detected values for the onset ($f_0$) and steady-state ($f_{inf}$) response are marked in \todo {color}. $f_0$ is detected as the highest deviation from the mean frequency before the stimulus while $f_{inf}$ is the average frequency in the 0.1\,s time window, 25\,ms before the end of the stimulus. On the right: The fi-curve visualizes the onset and steady-state response of the neuron for different stimuli contrasts. In \todo {color} the detected onset responses and the fitted Boltzmann, in \todo {color} the detected steady-state response and the linear fit}{figure.3}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.4}Leaky Integrate and Fire Model}{8}{subsection.4.4}}
\citation{benda2010linear}
\citation{benda2005spike}
\newlabel{eq:basic_voltage_dynamics}{{5}{9}{Leaky Integrate and Fire Model}{equation.4.5}{}}
\newlabel{eq:adaption_dynamics}{{6}{9}{Leaky Integrate and Fire Model}{equation.4.6}{}}
\newlabel{eq:currents_lifac}{{7}{9}{Leaky Integrate and Fire Model}{equation.4.7}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces  Comparison of different simple models normed to a spontaneous firing rate of ~10 Hz stimulated with a step stimulus. In the left column y-axis in mV in the right column the y-axis shows the frequency in Hz. PIF: Shows a continuously increasing membrane voltage with a fixed slope and as such constant frequency for a given stimulus strength. LIF: Approaches a stimulus dependent membrane voltage steady state exponentially Also has constant frequency for a fixed stimulus value. LIFAC: Exponentially approaches its new membrane voltage value but also shows adaption after changes in the stimulus the frequency takes some time to adapt and arrive at the new stable value. }}{10}{figure.4}}
\newlabel{fig:model_comparison}{{4}{10}{Comparison of different simple models normed to a spontaneous firing rate of ~10 Hz stimulated with a step stimulus. In the left column y-axis in mV in the right column the y-axis shows the frequency in Hz. PIF: Shows a continuously increasing membrane voltage with a fixed slope and as such constant frequency for a given stimulus strength. LIF: Approaches a stimulus dependent membrane voltage steady state exponentially Also has constant frequency for a fixed stimulus value. LIFAC: Exponentially approaches its new membrane voltage value but also shows adaption after changes in the stimulus the frequency takes some time to adapt and arrive at the new stable value}{figure.4}{}}
\newlabel{eq:full_voltage_dynamics}{{8}{10}{Leaky Integrate and Fire Model}{equation.4.8}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces  The stimulus modification in the model. The fish's EOD is simulated with a sin wave. It is rectified at the synapse and then further low-pass filtered in the dendrite.}}{11}{figure.5}}
\newlabel{fig:stim_development}{{5}{11}{The stimulus modification in the model. The fish's EOD is simulated with a sin wave. It is rectified at the synapse and then further low-pass filtered in the dendrite}{figure.5}{}}
\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces  Overview about all variables of the model that are fitted.}}{11}{table.1}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {4.5}Fitting of the Model}{11}{subsection.4.5}}
\citation{gao2012implementing}
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\bibcite{benda2005spike}{{1}{2005}{{Benda et~al.}}{{}}}
\bibcite{benda2010linear}{{2}{2010}{{Benda et~al.}}{{}}}
\bibcite{gao2012implementing}{{3}{2012}{{Gao and Han}}{{}}}
\bibcite{todd1999identification}{{4}{1999}{{Todd and Andrews}}{{}}}
\bibcite{walz2013Phd}{{5}{2013}{{Walz}}{{}}}
\bibcite{walz2014static}{{6}{2014}{{Walz et~al.}}{{}}}
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\@writefile{toc}{\contentsline {section}{\numberline {5}Results}{12}{section.5}}
\@writefile{toc}{\contentsline {section}{\numberline {6}Discussion}{12}{section.6}}