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add narrowband plots

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Dennis Huben 2024-09-26 18:15:15 +02:00
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@ -781,6 +781,8 @@ Bottom: Using the difference in coding fraction instead of the quotient makes th
\label{increases_broad}
\end{figure}
\subsubsection{Narrowband}
Qualitatively we see very similar results when instead of the broadband signal we use the narrowband signal with a frequency cutoff of 50Hz (figure \ref{overview_experiment_results_narrow}. Again the cells in the first quantile interval show on average only a very slightly increasing coding fraction with increasing population size. Coding fraction for a population size of one on average decreases for the higher quartile intervals. The seperate coding fraction curves also show the typical flatness for the first quartile interval. The fourth quartile interval in particular contains several curves that are only just beginning to increase in coding fraction at a population size of 64 neurons.
@ -791,6 +793,36 @@ Qualitatively we see very similar results when instead of the broadband signal w
\label{overview_experiment_results_narrow}
\end{figure}
%figures created with result_fits.py
\begin{figure}
%\includegraphics[width=0.45\linewidth]{img/ordnung/sigma_popsize_curves_0to300}
\centering
%\includegraphics[width=0.45\linewidth]{img/sigma/cf_N_ex_lines}
\includegraphics[width=0.45\linewidth]{img/sigma/narrow_0_50/scatter_and_fits_sigma_quot_firing_rate}%
\includegraphics[width=0.45\linewidth]{img/sigma/narrow_0_50/scatter_and_fits_firing_rate_quot_contrast}%
\includegraphics[width=0.45\linewidth]{img/sigma/narrow_0_50/scatter_and_fits_sigma_diff_firing_rate}%
\includegraphics[width=0.45\linewidth]{img/sigma/narrow_0_50/scatter_and_fits_firing_rate_diff_contrast}%
\caption{Top: The relative increase in coding fraction for population sizes 64 and 1. Note that the y-axis scales logarithmically Left: As a function of $\sigma$. Red curve shows a regression between $\sigma$ and $\log(c_{64}/c_{1})$. Right: As a function of cell firing rate.
Bottom: Using the difference in coding fraction instead of the quotient makes the relationship between the increase in coding fraction and the two parameters $\sigma$ and firing rate disappear. This might be different for larger population sizes.}
\label{increases_narow}
\end{figure}
%figures created with result_fits.py
\begin{figure}
\centering
\includegraphics[width=0.45\linewidth]{img/sigma/narrow_250_300/scatter_and_fits_sigma_quot_firing_rate}%
\includegraphics[width=0.45\linewidth]{img/sigma/narrow_250_300/scatter_and_fits_firing_rate_quot_contrast}%
\includegraphics[width=0.45\linewidth]{img/sigma/narrow_250_300/scatter_and_fits_sigma_diff_firing_rate}%
\includegraphics[width=0.45\linewidth]{img/sigma/narrow_250_300/scatter_and_fits_firing_rate_diff_contrast}%
\caption{Top: The relative increase in coding fraction for population sizes 64 and 1. Note that the y-axis scales logarithmically Left: As a function of $\sigma$. Red curve shows a regression between $\sigma$ and $\log(c_{64}/c_{1})$. Right: As a function of cell firing rate.
Bottom: Using the difference in coding fraction instead of the quotient makes the relationship between the increase in coding fraction and the two parameters $\sigma$ and firing rate disappear. This might be different for larger population sizes.}
\label{increases_narow}
\end{figure}