result images

thesis/Masterthesis.tex

@@ -161,13 +161,13 @@ When the fish's EOD is unperturbed P units fire every few EOD periods but they h
 
 
 \begin{figure}[H]
-{\caption{\label{fig:heterogeneity_isi_hist} \textbf{A--C} 100\,ms of cell membrane voltage and \textbf{D--F} interspike interval histograms, each for three different cells. Showing the variability between cells of spiking behavior of P units in baseline conditions. \textbf{A} and \textbf{D}: A non bursting cell with a baseline firing rate of 133\,Hz (EODf: 806\,Hz), \textbf{B} and \textbf{E}: A cell with some bursts and a baseline firing rate of 235\,Hz (EODf: 682\,Hz) and \textbf{C} and \textbf{F}: A strongly bursting cell with longer breaks between bursts. Baseline rate of  153\,Hz and EODf of 670\,Hz }}
-{\includegraphics[width=1\textwidth]{figures/isi_hist_heterogeneity.png}}
+{\caption{\label{fig:heterogeneity_isi_hist} Variability in spiking behavior between P units under baseline conditions. \textbf{A--C} 100\,ms of cell membrane voltage and \textbf{D--F} interspike interval histograms, each for three different cells. \textbf{A} and \textbf{D}: A non bursting cell with a baseline firing rate of 133\,Hz (EODf: 806\,Hz), \textbf{B} and \textbf{E}: A cell with some bursts and a baseline firing rate of 235\,Hz (EODf: 682\,Hz) and \textbf{C} and \textbf{F}: A strongly bursting cell with longer breaks between bursts. Baseline rate of  153\,Hz and EODf of 670\,Hz }}
+{\includegraphics[width=\textwidth]{figures/isi_hist_heterogeneity.png}}
 \end{figure}
 
 \todo{heterogeneity more}
 
-Furthermore show P units a pronounced heterogeneity in their spiking behavior (fig.~\ref{fig:heterogeneity_isi_hist}, \cite{gussin2007limits}). This is an important aspect one needs to consider when trying to understand what and how information is encoded in the spike trains of the neuron. A single neuron might be an independent unit from all other neurons but through different tuning curves a full picture of the stimulus can be encoded in the population while a single neuron only encodes a small feature space. This type of encoding is ubiquitous in the nervous system and is used in (EXAMPLE) for (EXAMPLE feature) PLUS MORE... \todo{refs}. Even though P units were already modelled based on a simple leaky integrate-and-fire neuron \citep{chacron2001simple} and conductance based \citep{kashimori1996model} and well studied (\todo{refS}), but up to this point there no model that tries to cover the full breadth of heterogeneity of the P unit population. Having such a model could help shed light into the population code used in the electric sense, allow researchers gain a better picture how higher brain areas might process the information and get one step closer to the full path between sensory input and behavioural output.
+Furthermore show P units a pronounced heterogeneity in their spiking behavior (fig.~\ref{fig:heterogeneity_isi_hist}, \cite{gussin2007limits}). This is an important aspect one needs to consider when trying to understand what and how information is encoded in the spike trains of the neuron. A single neuron might be an independent unit from all other neurons but through different tuning curves a full picture of the stimulus can be encoded in the population while a single neuron only encodes a small feature space. This type of encoding is ubiquitous in the nervous system and is used in the visual sense for color vision,  PLUS MORE... \todo{refs}. Even though P units were already modelled based on a simple leaky integrate-and-fire neuron \citep{chacron2001simple} and conductance based \citep{kashimori1996model} and well studied (\todo{refS}), but up to this point there no model that tries to cover the full breadth of heterogeneity of the P unit population. Having such a model could help shed light into the population code used in the electric sense, allow researchers gain a better picture how higher brain areas might process the information and get one step closer to the full path between sensory input and behavioural output.
 
 
 
@@ -371,7 +371,7 @@ Together this results in the dynamics seen in equations \ref{eq:full_model_dynam
  parameter & explanation & unit \\
  \hline
   $\alpha$   	&	stimulus scaling factor & [cm] \\
- $tau_m$    	&	membrane time constant & [ms]\\
+ $\tau_m$    	&	membrane time constant & [ms]\\
  $I_{Bias}$ 	& 	bias current & [mV] \\
  $\sqrt{2D}$ 	&	noise strength & [mV$\sqrt{\text{s}}$]\\
  $\tau_A$ 		&	adaption time constant & [ms] \\
@@ -447,13 +447,43 @@ All errors were then summed up for the full error. The fits were done with the N
 
 \section{Results}
 
+\begin{figure}[H]
+\includegraphics[scale=0.5]{figures/fit_baseline_comparison.png}
+\caption{\label{fig:comp_baseline} }
+\end{figure}
+
+\begin{figure}[H]
+\includegraphics[scale=0.5]{figures/fit_adaption_comparison.png}
+\caption{\label{fig:comp_adaption} Excluded 8 value pairs from Onset Slope as they had slopes higher than 30000}
+\end{figure}
+
+\begin{figure}[H]
+\includegraphics[scale=0.5]{figures/fit_burstiness_comparison.png}
+\caption{\label{fig:comp_burstiness} }
+\end{figure}
+
+
+\begin{figure}[H]
+\includegraphics[scale=0.6]{figures/behaviour_correlations.png}
+\caption{\label{fig:behavior_correlations} Additional $f_{\infty}$ correlation hängt vermutlich mit der bursty-baseline freq correlation zusammen. Je höher die feuerrate umso höher chance zu bursten und bursts haben eine stärker negative SC.}
+\end{figure}
+
+\begin{figure}[H]
+\includegraphics[scale=0.6]{figures/parameter_distributions.png}
+\caption{\label{fig:parameter_distributions} }
+\end{figure}
+
+\begin{figure}[H]
+\includegraphics[scale=0.6]{figures/parameter_correlations.png}
+\caption{\label{fig:parameter_correlations} }
+\end{figure}
 
 
 \section{Discussion}
 
 
 
-
+\newpage
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