dennis/role_of_noise
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Add the narrowband in broadband fish analysis

Browse Source
This commit is contained in:
Dennis Huben 2024-09-26 18:31:07 +02:00
parent c99324a5a1
commit d6b45c9ead
1 changed files with 17 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

20
main.tex
View File

@ -793,11 +793,11 @@ Qualitatively we see very similar results when instead of the broadband signal w
\label{overview_experiment_results_narrow}
\end{figure}
Figures \ref{increases_narow} and \ref{increases_narow_high} both show that the results with regards to the increase of coding fraction for different population sizes seen for the broadband signal also appear when we use narrowband signals.
%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}%
@ -819,11 +819,25 @@ Bottom: Using the difference in coding fraction instead of the quotient makes th
\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}
\label{increases_narow_high}
\end{figure}
\notedh{link to the appropriate chapter from theory results}
In addition to the ``pure'' narrowband signals, I also analysed the coding fraction change for a smaller part of the spectrum in the experiments using the broadband signal. Figure \ref{increases_narow_in_broad} shows part of the results and again we see the strong correlation between $\sigma$ and the gain and a lesser correlation between the firing rate and the gain. In this case we see the same correlation also for the coding fraction difference.
Similar results can be observed for the other frequency bands. \notedh{Images to the appendix? The sigma/gain of all in one plot?}
%figures created with result_fits.py
\begin{figure}
\centering
\includegraphics[width=0.45\linewidth]{img/sigma/0_50/scatter_and_fits_sigma_quot_firing_rate}%
\includegraphics[width=0.45\linewidth]{img/sigma/0_50/scatter_and_fits_firing_rate_quot_contrast}%
\includegraphics[width=0.45\linewidth]{img/sigma/0_50/scatter_and_fits_sigma_diff_firing_rate}%
\includegraphics[width=0.45\linewidth]{img/sigma/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.}
\label{increases_narow_in_broad}
\end{figure}