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move firing rate/sigma to a more sensible place

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Dennis Huben 2024-09-26 18:18:59 +02:00
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@ -693,7 +693,14 @@ We can see from figure \ref{sigmafits_example} that the fits look very close to
\label{sigmafits_example}
\end{figure}
Figure \ref{fr_sigma} shows that between the firing rate and the cell and its noisiness is only a very weak correlation and they appear mostly independent of each other.
\begin{figure}
\includegraphics[width=0.45\linewidth]{img/sigma/0_300/scatter_and_fits_sigma_firing_rate_contrast.pdf}
\includegraphics[width=0.45\linewidth]{img/sigma/0_300/scatter_and_fits_contrast_firing_rate_sigma.pdf}
\caption{Relationship between firing rate and $\sigma$ and cv respectively. Noisier tend to be cells that fire slower, but the relationship is very slight.}
\label{fr_sigma}
\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.
@ -740,23 +747,16 @@ The curves from which the averages were created can be seen in figure \ref{2_by_
Figure \ref{coding_fraction_n_1} shows the link between noisiness and coding fraction very apparently. There is a strong correlation between the coding fraction calculated from the response of a single neuron and the neuron's noisiness. This intuitively makes sense, because the SSR advantage noisiness offers that we discussed earlier only appears for populations. There is a smaller, but still obvious, correlation between the coding fraction and the cell's firing rate: An increase in firing rate increases the coding fraction.
Between the firing rate and the cell and its noisiness is only a very weak correlation and they appear mostly independent of each other (figure \ref{fr_sigma}.
\begin{figure}
\includegraphics[width=0.45\linewidth]{img/sigma/0_300/scatter_and_fits_sigma_coding_fractions_firing_rate.pdf}
\includegraphics[width=0.45\linewidth]{img/sigma/0_300/scatter_and_fits_firing_rate_coding_fractions_sigma.pdf}
\caption{Low firing rate and strong noise bith lead a a small coding fraction for single neurons.}
\caption{Low firing rate and strong noise both lead a a small coding fraction for single neurons.}
\label{coding_fraction_n_1}
\end{figure}
\begin{figure}
\includegraphics[width=0.45\linewidth]{img/sigma/0_300/scatter_and_fits_sigma_firing_rate_contrast.pdf}
\includegraphics[width=0.45\linewidth]{img/sigma/0_300/scatter_and_fits_contrast_firing_rate_sigma.pdf}
\caption{Relationship between firing rate and $\sigma$ and cv respectively. Noisier tend to be cells that fire slower, but the relationship is very slight.}
\label{fr_sigma}
\end{figure}
We can further quantify the effect of SSR on the encoding by studying the difference in coding fraction for populations of different sizes. There are two different ways to do this. The first is to take the coding fraction at a large population size (here: 64 neurons) and divide it by the coding fraction for a single neuron. It is important to note that a large gain does not necessarily mean a good performance: a neuron that starts with a coding fraction of 0.01 for a population size of 1 can have a gain of 10. It would still perform worse for a population of 64 neurons than a cell that starts with a coding fraction of 0.11 even though that cell will certainly have a gain lower than 10, as coding fraction is limited at 1.