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Changed some figure captions

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Dennis Huben 2024-09-04 16:58:58 +02:00
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1 changed files with 9 additions and 7 deletions
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@ -580,6 +580,12 @@ We created populations out of each cell. For each p-unit, we took the different
}
\end{figure}
\begin{figure}
\includegraphics[width=0.65\linewidth]{img/sigma/cf_N_sigma.pdf}
\caption{Coding fraction as a function of population size. Each line represents one cell. Populations were created by taking separate trials from each cell. Line color indicates the firing rate of the cell. \notedh{What are they sorted/divided by? $\sigma$?}}
\label{Curve_examples}
\end{figure}
@ -676,16 +682,12 @@ We can see from figure \ref{sigmafits_example} that the fits look very close to
\includegraphics[width=0.23\linewidth]{img/simulation_sigma_examples/fitcurve_50hz_0.001noi500s_0_capped.pdf}
\includegraphics[width=0.23\linewidth]{img/simulation_sigma_examples/fitcurve_50hz_0.1noi500s_0_capped.pdf}
% cropped_fitcurve_0_2010-08-31-ad-invivo-1_0.pdf: 0x0 px, 300dpi, 0.00x0.00 cm, bb=
\caption{Histogram of spike count distribution (firing rate) and errorfunction fits. 50 bins represent different values of the Gaussian distributed input signal [maybe histogram in background again]. The value of each of those bins is the number of spikes during the times the signal was in that bin. Each of the values was normalized by the signal distribution. To account for delay, we first calculated the cross-correlation of signal and spike train and took its peak as the delay. The lines show fits according to equation \eqref{errorfct}. Left and right plots show two different cells, one with a relatively narrow distribution and one with a distribution that is more broad, as indicated by the parameter \(\sigma\). Different amounts of bins (30 and 100) showed no difference in resulting parameters. \notedh{Show a plot.} \notedh{Show more than two plots?}}
\caption{Histogram of spike count distribution (firing rate) and errorfunction fits. 50 bins represent different values of the amplitude of the Gaussian distributed input signal \notedh{[maybe histogram in background again] - or better: One plot where I show the raw data - histrogram in background, number of spikes as dots.}. The value of each of those bins is the number of spikes during the times the signal was in that bin. Each of the values was normalized by the signal distribution. To account for delay, we first calculated the cross-correlation of signal and spike train and took its peak as the delay. The lines show fits according to equation \eqref{errorfct}.
Below are some examples of different cells, with differently wide distributions. one with a relatively narrow distribution and one with a distribution that is more broad, as indicated by the parameter \(\sigma\). Different amounts of bins (30 and 100) showed no difference in resulting parameters. \notedh{Show a plot.} \notedh{Show more than two plots?}}
\label{sigmafits_example}
\end{figure}
\begin{figure}
\includegraphics[width=0.65\linewidth]{img/sigma/cf_N_sigma.pdf}
\caption{Coding fraction as a function of population size. Each line represents one cell. Populations were created by taking seperate trials from each cell. \notedh{What are they sorted/divided by?}}
\label{Curve_examples}
\end{figure}
%TODO insert plot with sigma x-axis and delta_cf on y-axis here; also, plot with sigma as function of firing rate, also absoulte cf for different population size as function of sigma.
@ -817,7 +819,7 @@ to the coding fraction in a single cell is larger for higher frequencies.
\includegraphics[width=0.49\linewidth]{img/fish/ratio_narrow.pdf}
\includegraphics[width=0.49\linewidth]{img/fish/broad_ratio.pdf}
\label{freq_delta_cf}
\caption{This is about frequency and how it determines $delta_cf$. In other paper I have used $quot_cf$. \notedh{The x-axis labels don't make sense to me.}}
\caption{This is about frequency and how it determines $delta_cf$. In other paper I have used $quot_cf$. \notedh{The x-axis labels don't make sense to me. Left is broad and right is narrow? }}
\end{figure}
\subsection{Discussion}