diff --git a/figures/fig_features_cross_species.pdf b/figures/fig_features_cross_species.pdf index c4cb861..1450418 100644 Binary files a/figures/fig_features_cross_species.pdf and b/figures/fig_features_cross_species.pdf differ diff --git a/figures/fig_invariance_thresh_lp_single.pdf b/figures/fig_invariance_thresh_lp_single.pdf index 372a9b9..f24160d 100644 Binary files a/figures/fig_invariance_thresh_lp_single.pdf and b/figures/fig_invariance_thresh_lp_single.pdf differ diff --git a/main.aux b/main.aux index 66d8a5a..f2e9716 100644 --- a/main.aux +++ b/main.aux @@ -172,7 +172,8 @@ \abx@aux@page{48}{4} \abx@aux@page{49}{4} \abx@aux@page{50}{4} -\@writefile{toc}{\contentsline {section}{\numberline {2}Developing a functional model of the\\grasshopper song recognition pathway}{4}{}\protected@file@percent } +\@writefile{toc}{\contentsline {section}{\numberline {2}Methods}{4}{}\protected@file@percent } +\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}Functional model of the grasshopper song recognition pathway}{4}{}\protected@file@percent } \abx@aux@cite{0}{windmill2008time} \abx@aux@segm{0}{0}{windmill2008time} \abx@aux@cite{0}{malkin2014energy} @@ -189,7 +190,7 @@ \abx@aux@page{56}{5} \abx@aux@page{57}{5} \abx@aux@page{58}{5} -\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}Population-driven signal preprocessing}{5}{}\protected@file@percent } +\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.1.1}Population-driven signal preprocessing}{5}{}\protected@file@percent } \abx@aux@page{59}{5} \abx@aux@page{60}{5} \newlabel{eq:bandpass}{{1}{5}{}{}{}} @@ -224,7 +225,7 @@ \newlabel{eq:highpass}{{4}{7}{}{}{}} \@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces \textbf {Representations of a song of \textit {O. rufipes} during the preprocessing stage.} \textbf {a}:~Bandpass filtered tympanal signal $x_{\text {filt}}(t)$. \textbf {b}:~Signal envelope $x_{\text {env}}(t)$. \textbf {c}:~Logarithmically compressed envelope $x_{\text {log}}(t)$. \textbf {d}:~Intensity-adapted envelope $x_{\text {adapt}}(t)$. }}{8}{}\protected@file@percent } \newlabel{fig:stages_pre}{{2}{8}{}{}{}} -\@writefile{toc}{\contentsline {subsection}{\numberline {2.2}Feature extraction by individual neurons}{8}{}\protected@file@percent } +\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.1.2}Feature extraction by individual neurons}{8}{}\protected@file@percent } \newlabel{eq:conv}{{5}{8}{}{}{}} \newlabel{eq:gabor}{{6}{8}{}{}{}} \abx@aux@cite{0}{ronacher1986routes} @@ -242,11 +243,13 @@ \newlabel{eq:lowpass}{{10}{10}{}{}{}} \@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces \textbf {Representations of a song of \textit {O. rufipes} during the feature extraction stage.} Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; two kernel widths $\sigma $ of $4\,$ms and $32\,$ms per type; 8 types, 16 kernels in total). \textbf {a}:~Kernel-specific filter responses $c_i(t)$. \textbf {b}:~Binary responses $b_i(t)$. \textbf {c}:~Finalized features $f_i(t)$.}}{10}{}\protected@file@percent } \newlabel{fig:stages_feat}{{3}{10}{}{}{}} -\@writefile{toc}{\contentsline {section}{\numberline {3}Mechanisms driving the emergence of\\intensity-invariant song representation}{10}{}\protected@file@percent } -\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Full-wave rectification \& lowpass filtering}{11}{}\protected@file@percent } +\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.1.3}Simulation-based analysis of the model pathway}{10}{}\protected@file@percent } +\@writefile{toc}{\contentsline {section}{\numberline {3}Results}{10}{}\protected@file@percent } +\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Mechanisms driving the emergence of intensity invariance}{10}{}\protected@file@percent } +\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.1.1}Full-wave rectification \& lowpass filtering}{11}{}\protected@file@percent } \@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces \textbf {Rectification and lowpass filtering improves SNR but does not contribute to intensity invariance.} Input $x_{\text {raw}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with optional noise component $\eta (t)$ and is successively transformed into tympanal signal $x_{\text {filt}}(t)$ and envelope $x_{\text {env}}(t)$. Different line styles indicate different cutoff frequencies $f_{\text {cut}}$ of the lowpass filter extracting $x_{\text {env}}(t)$. \textbf {Top}:~Example representations of $x_{\text {filt}}(t)$ and $x_{\text {env}}(t)$ for different $\alpha $. \textbf {a}:~Noiseless case. \textbf {b}:~Noisy case. \textbf {Bottom}:~Intensity metrics over a range of $\alpha $. \textbf {c}:~Noiseless case: Standard deviations $\sigma _x$ of $x_{\text {filt}}(t)$ and $x_{\text {env}}(t)$. \textbf {d}:~Noisy case: Ratios of $\sigma _x$ of $x_{\text {filt}}(t)$ and $x_{\text {env}}(t)$ to the respective reference standard deviation $\sigma _{\eta }$ for input $x_{\text {raw}}(t)=\eta (t)$. \textbf {e}:~Ratios of $\sigma _x$ to $\sigma _{\eta }$ of $x_{\text {env}}(t)$ as in \textbf {d} for different species (averaged over songs and recordings, see appendix Fig.\,\ref {fig:app_rect-lp}). }}{12}{}\protected@file@percent } \newlabel{fig:rect-lp}{{4}{12}{}{}{}} -\@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Logarithmic compression \& spike-frequency adaptation}{12}{}\protected@file@percent } +\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.1.2}Logarithmic compression \& spike-frequency adaptation}{13}{}\protected@file@percent } \newlabel{eq:toy_env_pure}{{11}{13}{}{}{}} \newlabel{eq:toy_log_pure}{{12}{13}{}{}{}} \newlabel{eq:toy_highpass_pure}{{13}{13}{}{}{}} @@ -255,48 +258,54 @@ \newlabel{eq:toy_highpass_noise}{{16}{14}{}{}{}} \@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces \textbf {Intensity invariance through logarithmic compression and adaptation is restricted by the noise floor and decreases SNR.} Input $x_{\text {filt}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with optional noise component $\eta (t)$ and is successively transformed into envelope $x_{\text {env}}(t)$, logarithmically compressed envelope $x_{\text {log}}(t)$, and intensity-adapted envelope $x_{\text {adapt}}(t)$. \textbf {Top}:~Example representations of $x_{\text {env}}(t)$, $x_{\text {log}}(t)$, and $x_{\text {adapt}}(t)$ for different $\alpha $. \textbf {a}:~Noiseless case. \textbf {b}:~Noisy case. \textbf {Bottom}:~Intensity metrics over a range of $\alpha $. \textbf {c}:~Noiseless case: Standard deviations $\sigma _x$ of $x_{\text {env}}(t)$, $x_{\text {log}}(t)$, and $x_{\text {adapt}}(t)$. \textbf {d}:~Noisy case: Ratios of $\sigma _x$ of $x_{\text {env}}(t)$, $x_{\text {log}}(t)$, and $x_{\text {adapt}}(t)$ to the respective reference standard deviation $\sigma _{\eta }$ for input $x_{\text {filt}}(t)=\eta (t)$. Shaded areas indicate $5\,\%$ (dark grey) and $95\,\%$ (light grey) curve span for $x_{\text {adapt}}(t)$. \textbf {e}:~Ratios of $\sigma _x$ to $\sigma _{\eta }$ of $x_{\text {adapt}}(t)$ as in \textbf {d} for different species (averaged over songs and recordings, see appendix Fig\,\ref {fig:app_log-hp_curves}). Dots indicate $95\,\%$ curve span per species. }}{15}{}\protected@file@percent } \newlabel{fig:log-hp}{{5}{15}{}{}{}} -\@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Thresholding nonlinearity \& temporal averaging}{16}{}\protected@file@percent } +\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.1.3}Thresholding nonlinearity \& temporal averaging}{16}{}\protected@file@percent } \newlabel{eq:pdf_split}{{17}{16}{}{}{}} \newlabel{eq:feat_avg}{{18}{16}{}{}{}} \newlabel{eq:feat_prop}{{19}{16}{}{}{}} \@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces \textbf {Intensity invariance through thresholding and temporal averaging is mediated by the interaction of threshold value and noise floor.} Input $x_{\text {adapt}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with optional noise component $\eta (t)$ and is transformed into single kernel response $c(t)$, binary response $b(t)$, and feature $f(t)$. Different color shades indicate different threshold values $\Theta $ (multiples of reference standard deviation $\sigma _{\eta }$ of $c(t)$ for input $x_{\text {adapt}}(t)=\eta (t)$, with darker colors for higher $\Theta $). \textbf {Left}:~Noisy case: Example representations of $x_{\text {adapt}}(t)$ as well as $c(t)$, $b(t)$, and $f(t)$ for different $\alpha $. \textbf {a}:~$x_{\text {adapt}}(t)$ with kernel $k(t)$ in black. \textbf {b\,-\,d}: $c(t)$, $b(t)$, and $f(t)$ based on the same $x_{\text {adapt}}(t)$ from \textbf {a} but with different $\Theta $. \textbf {Right}:~Average value $\mu _f$ of $f(t)$ for each $\Theta $ from \textbf {b\,-\,d}. Dots indicate $95\,\%$ curve span (noisy case). \textbf {e}:~$\mu _f$ over a range of $\alpha $, once for the noisy case (solid lines) and once for the noiseless case (dotted lines). \textbf {f}:~Noisy case: $\mu _f$ over the standard deviation of input $x_{\text {adapt}}$ corresponding to the values of $\alpha $ shown in \textbf {e}. Shaded area indicates standard deviations that would be capped in the output $x_{\text {adapt}}(t)$ of the previous transformation pair (see Fig.\,\ref {fig:log-hp}cd). }}{18}{}\protected@file@percent } \newlabel{fig:thresh-lp_single}{{6}{18}{}{}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces \textbf {Feature representation of different species-specific songs saturates at different points in feature space.} Same input and processing as in Fig.\,\ref {fig:thresh-lp_single} but with three different kernels $k_i$, each with a single kernel-specific threshold value $\Theta _i=0.5\cdot \sigma _{\eta _i}$. \textbf {a}:~Examples of species-specific grasshopper songs. \textbf {Middle}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $ per species (averaged over songs and recordings, see appendix Figs.\,\ref {fig:app_thresh-lp_pure} and \ref {fig:app_thresh-lp_noise}). Different color shades indicate different kernels $k_i$. Dots indicate $95\,\%$ curve span per $k_i$. \textbf {b}:~Noiseless case. \textbf {c}:~Noisy case. \textbf {Bottom}:~2D feature spaces spanned by each pair of $f_i(t)$. Each trajectory corresponds to a species-specific combination of $\mu _{f_i}$ that develops with $\alpha $ (colorbars). Horizontal dashes in the colorbar indicate $5\,\%$ (dark grey) and $95\,\%$ (light grey) curve span of the norm across all three $\mu _{f_i}$ per species. \textbf {d}:~Noiseless case. \textbf {e}:~Noisy case. Shaded areas indicate the average minimum $\mu _{f_i}$ across all species-specific trajectories. }}{19}{}\protected@file@percent } -\newlabel{fig:thresh-lp_species}{{7}{19}{}{}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces \textbf {Step-wise emergence of intensity-invariant song representation along the full model pathway.} Input $x_{\text {raw}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with added noise component $\eta (t)$ and is processed up to the feature set $f_i(t)$. Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). \textbf {a}:~Example representations of $x_{\text {filt}}(t)$, $x_{\text {env}}(t)$, $x_{\text {log}}(t)$, $x_{\text {adapt}}(t)$, $c_i(t)$, and $f_i(t)$ for different $\alpha $. \textbf {b}:~Intensity metrics over $\alpha $. For $c_i(t)$ and $f_i(t)$, the median over kernels is shown. Dots indicate $95\,\%$ curve span for $x_{\text {log}}(t)$, $x_{\text {adapt}}(t)$, $c_i(t)$, and $f_i(t)$. \textbf {c}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $. \textbf {d}:~Ratios of intensity metrics to the respective reference value for input $x_{\text {raw}}(t)=\eta (t)$. For $c_i(t)$ and $f_i(t)$, the median over kernel-specific ratios is shown. \textbf {e}:~Ratios of standard deviation $\sigma _{c_i}$ of each $c_i(t)$. \textbf {f}:~Ratios of $\mu _{f_i}$. \textbf {g}:~Distributions of kernel-specific $\alpha $ that correspond to $95\,\%$ curve span for $c_i(t)$ and $f_i(t)$. Dots indicate the values from \textbf {b}. }}{20}{}\protected@file@percent } -\newlabel{fig:pipeline_full}{{8}{20}{}{}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces \textbf {Step-wise emergence of intensity invariant song representation along the model pathway without logarithmic compression.} Input $x_{\text {raw}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with added noise component $\eta (t)$ and is processed up to the feature set $f_i(t)$, skipping $x_{\text {log}}(t)$. Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). \textbf {a}:~Example representations of $x_{\text {filt}}(t)$, $x_{\text {env}}(t)$, $x_{\text {adapt}}(t)$, $c_i(t)$, and $f_i(t)$ for different $\alpha $. \textbf {b}:~Intensity metrics over $\alpha $. For $c_i(t)$ and $f_i(t)$, the median over kernels is shown. Dots indicate $95\,\%$ curve span for $f_i(t)$. \textbf {c}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $. \textbf {d}:~Ratios of intensity metrics to the respective reference value for input $x_{\text {raw}}(t)=\eta (t)$. For $c_i(t)$ and $f_i(t)$, the median over kernel-specific ratios is shown. \textbf {e}:~Ratios of $\mu _{f_i}$. \textbf {f}:~Distribution of kernel-specific $\alpha $ that correspond to $95\,\%$ curve span for $f_i(t)$. Dots indicate the value from \textbf {b}. }}{21}{}\protected@file@percent } -\newlabel{fig:pipeline_short}{{9}{21}{}{}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {10}{\ignorespaces \textbf {Interspecific and intraspecific feature variability.} Average value $\mu _{f_i}$ of each feature $f_i(t)$ against its counterpart from a 2nd feature set based on a different input $x_{\text {raw}}(t)$. Each dot within a subplot represents a single feature $f_i(t)$. Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). Data is based on the analysis underlying Fig\,\ref {fig:pipeline_full}. \textbf {Lower triangular}:~Interspecific comparisons between single songs of different species. \textbf {Upper triangular}:~Intraspecific comparisons between different songs of a single species (\textit {O. rufipes}). \textbf {Lower left}:~Distribution of correlation coefficients $\rho $ for each interspecific and intraspecific comparison. Dots indicate single $\rho $ values. }}{22}{}\protected@file@percent } -\newlabel{fig:feat_cross_species}{{10}{22}{}{}{}} +\@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Intensity invariance of species-specific feature representations}{19}{}\protected@file@percent } +\@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces \textbf {Feature representation of different species-specific songs saturates at different points in feature space.} Same input and processing as in Fig.\,\ref {fig:thresh-lp_single} but with three different kernels $k_i$, each with a single kernel-specific threshold value $\Theta _i=0.5\cdot \sigma _{\eta _i}$. \textbf {a}:~Examples of species-specific grasshopper songs. \textbf {Middle}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $ per species (averaged over songs and recordings, see appendix Figs.\,\ref {fig:app_thresh-lp_pure} and \ref {fig:app_thresh-lp_noise}). Different color shades indicate different kernels $k_i$. Dots indicate $95\,\%$ curve span per $k_i$. \textbf {b}:~Noiseless case. \textbf {c}:~Noisy case. \textbf {Bottom}:~2D feature spaces spanned by each pair of $f_i(t)$. Each trajectory corresponds to a species-specific combination of $\mu _{f_i}$ that develops with $\alpha $ (colorbars). Horizontal dashes in the colorbar indicate $5\,\%$ (dark grey) and $95\,\%$ (light grey) curve span of the norm across all three $\mu _{f_i}$ per species. \textbf {d}:~Noiseless case. \textbf {e}:~Noisy case. Shaded areas indicate the average minimum $\mu _{f_i}$ across all species-specific trajectories. }}{21}{}\protected@file@percent } +\newlabel{fig:thresh-lp_species}{{7}{21}{}{}{}} +\@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Intensity invariance along the full model pathway}{22}{}\protected@file@percent } +\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.3.1}Including logarithmic compression}{22}{}\protected@file@percent } +\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces \textbf {Step-wise emergence of intensity-invariant song representation along the full model pathway.} Input $x_{\text {raw}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with added noise component $\eta (t)$ and is processed up to the feature set $f_i(t)$. Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). \textbf {a}:~Example representations of $x_{\text {filt}}(t)$, $x_{\text {env}}(t)$, $x_{\text {log}}(t)$, $x_{\text {adapt}}(t)$, $c_i(t)$, and $f_i(t)$ for different $\alpha $. \textbf {b}:~Intensity metrics over $\alpha $. For $c_i(t)$ and $f_i(t)$, the median over kernels is shown. Dots indicate $95\,\%$ curve span for $x_{\text {log}}(t)$, $x_{\text {adapt}}(t)$, $c_i(t)$, and $f_i(t)$. \textbf {c}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $. \textbf {d}:~Ratios of intensity metrics to the respective reference value for input $x_{\text {raw}}(t)=\eta (t)$. For $c_i(t)$ and $f_i(t)$, the median over kernel-specific ratios is shown. \textbf {e}:~Ratios of standard deviation $\sigma _{c_i}$ of each $c_i(t)$. \textbf {f}:~Ratios of $\mu _{f_i}$. \textbf {g}:~Distributions of kernel-specific $\alpha $ that correspond to $95\,\%$ curve span for $c_i(t)$ and $f_i(t)$. Dots indicate the values from \textbf {b}. }}{24}{}\protected@file@percent } +\newlabel{fig:pipeline_full}{{8}{24}{}{}{}} +\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.3.2}Excluding logarithmic compression}{25}{}\protected@file@percent } +\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces \textbf {Step-wise emergence of intensity invariant song representation along the model pathway without logarithmic compression.} Input $x_{\text {raw}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with added noise component $\eta (t)$ and is processed up to the feature set $f_i(t)$, skipping $x_{\text {log}}(t)$. Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). \textbf {a}:~Example representations of $x_{\text {filt}}(t)$, $x_{\text {env}}(t)$, $x_{\text {adapt}}(t)$, $c_i(t)$, and $f_i(t)$ for different $\alpha $. \textbf {b}:~Intensity metrics over $\alpha $. For $c_i(t)$ and $f_i(t)$, the median over kernels is shown. Dots indicate $95\,\%$ curve span for $f_i(t)$. \textbf {c}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $. \textbf {d}:~Ratios of intensity metrics to the respective reference value for input $x_{\text {raw}}(t)=\eta (t)$. For $c_i(t)$ and $f_i(t)$, the median over kernel-specific ratios is shown. \textbf {e}:~Ratios of $\mu _{f_i}$. \textbf {f}:~Distribution of kernel-specific $\alpha $ that correspond to $95\,\%$ curve span for $f_i(t)$. Dots indicate the value from \textbf {b}. }}{26}{}\protected@file@percent } +\newlabel{fig:pipeline_short}{{9}{26}{}{}{}} +\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.3.3}Field data}{27}{}\protected@file@percent } +\@writefile{lof}{\contentsline {figure}{\numberline {10}{\ignorespaces \textbf {Step-wise emergence of intensity invariant song representation along the model pathway.} }}{27}{}\protected@file@percent } +\newlabel{fig:pipeline_field}{{10}{27}{}{}{}} +\@writefile{toc}{\contentsline {subsection}{\numberline {3.4}Interspecific and intraspecific feature variability}{27}{}\protected@file@percent } +\@writefile{lof}{\contentsline {figure}{\numberline {11}{\ignorespaces \textbf {Interspecific and intraspecific feature variability.} Average value $\mu _{f_i}$ of each feature $f_i(t)$ against its counterpart from a 2nd feature set based on a different input $x_{\text {raw}}(t)$. Each dot within a subplot represents a single feature $f_i(t)$. Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). Data is based on the analysis underlying Fig\,\ref {fig:pipeline_full}. \textbf {Lower triangular}:~Interspecific comparisons between single songs of different species. \textbf {Upper triangular}:~Intraspecific comparisons between different songs of a single species (\textit {O. rufipes}). \textbf {Lower right}:~Distribution of correlation coefficients $\rho $ for each interspecific and intraspecific comparison. Dots indicate single $\rho $ values. }}{29}{}\protected@file@percent } +\newlabel{fig:feat_cross_species}{{11}{29}{}{}{}} \abx@aux@cite{0}{stumpner1991auditory} \abx@aux@segm{0}{0}{stumpner1991auditory} -\@writefile{lof}{\contentsline {figure}{\numberline {11}{\ignorespaces \textbf {Step-wise emergence of intensity invariant song representation along the model pathway.} }}{23}{}\protected@file@percent } -\newlabel{fig:pipeline_field}{{11}{23}{}{}{}} -\@writefile{toc}{\contentsline {section}{\numberline {4}Conclusions \& outlook}{23}{}\protected@file@percent } -\abx@aux@page{73}{23} -\@writefile{lof}{\contentsline {figure}{\numberline {12}{\ignorespaces \textbf {} }}{25}{}\protected@file@percent } -\newlabel{fig:app_env-sd}{{12}{25}{}{}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {13}{\ignorespaces \textbf {} }}{25}{}\protected@file@percent } -\newlabel{fig:app_rect-lp}{{13}{25}{}{}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {14}{\ignorespaces \textbf {} }}{26}{}\protected@file@percent } -\newlabel{fig:app_log-hp_curves}{{14}{26}{}{}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {15}{\ignorespaces \textbf {} }}{26}{}\protected@file@percent } -\newlabel{fig:app_log-hp_saturation}{{15}{26}{}{}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {16}{\ignorespaces \textbf {} }}{27}{}\protected@file@percent } -\newlabel{fig:app_thresh-lp_pure}{{16}{27}{}{}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {17}{\ignorespaces \textbf {} }}{27}{}\protected@file@percent } -\newlabel{fig:app_thresh-lp_noise}{{17}{27}{}{}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {18}{\ignorespaces \textbf {} }}{28}{}\protected@file@percent } -\newlabel{fig:app_thresh-lp_kern-sd}{{18}{28}{}{}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {19}{\ignorespaces \textbf {} }}{28}{}\protected@file@percent } -\newlabel{fig:app_full_kern-sd}{{19}{28}{}{}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {20}{\ignorespaces \textbf {} }}{29}{}\protected@file@percent } -\newlabel{fig:app_short_kern-sd}{{20}{29}{}{}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {21}{\ignorespaces \textbf {} }}{29}{}\protected@file@percent } -\newlabel{fig:app_field_kern-sd}{{21}{29}{}{}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {22}{\ignorespaces \textbf {} }}{30}{}\protected@file@percent } -\newlabel{fig:app_cross_species_thresh}{{22}{30}{}{}{}} +\@writefile{toc}{\contentsline {section}{\numberline {4}Conclusions \& outlook}{30}{}\protected@file@percent } +\abx@aux@page{73}{30} +\@writefile{lof}{\contentsline {figure}{\numberline {12}{\ignorespaces \textbf {} }}{31}{}\protected@file@percent } +\newlabel{fig:app_env-sd}{{12}{31}{}{}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {13}{\ignorespaces \textbf {} }}{32}{}\protected@file@percent } +\newlabel{fig:app_rect-lp}{{13}{32}{}{}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {14}{\ignorespaces \textbf {} }}{32}{}\protected@file@percent } +\newlabel{fig:app_log-hp_curves}{{14}{32}{}{}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {15}{\ignorespaces \textbf {} }}{33}{}\protected@file@percent } +\newlabel{fig:app_log-hp_saturation}{{15}{33}{}{}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {16}{\ignorespaces \textbf {} }}{33}{}\protected@file@percent } +\newlabel{fig:app_thresh-lp_pure}{{16}{33}{}{}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {17}{\ignorespaces \textbf {} }}{34}{}\protected@file@percent } +\newlabel{fig:app_thresh-lp_noise}{{17}{34}{}{}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {18}{\ignorespaces \textbf {} }}{34}{}\protected@file@percent } +\newlabel{fig:app_thresh-lp_kern-sd}{{18}{34}{}{}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {19}{\ignorespaces \textbf {} }}{35}{}\protected@file@percent } +\newlabel{fig:app_full_kern-sd}{{19}{35}{}{}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {20}{\ignorespaces \textbf {} }}{35}{}\protected@file@percent } +\newlabel{fig:app_short_kern-sd}{{20}{35}{}{}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {21}{\ignorespaces \textbf {} }}{36}{}\protected@file@percent } +\newlabel{fig:app_field_kern-sd}{{21}{36}{}{}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {22}{\ignorespaces \textbf {} }}{37}{}\protected@file@percent } +\newlabel{fig:app_cross_species_thresh}{{22}{37}{}{}{}} \gdef\svg@ink@ver@settings{{\m@ne }{inkscape}{\m@ne }} \abx@aux@read@bbl@mdfivesum{1380DC8C93D2855FDB132CC5A40AD52F} -\gdef \@abspage@last{30} +\gdef \@abspage@last{37} diff --git a/main.blg b/main.blg index 52ce8c2..81a5cea 100644 --- a/main.blg +++ b/main.blg @@ -1,71 +1,71 @@ [0] Config.pm:307> INFO - This is Biber 2.19 [0] Config.pm:310> INFO - Logfile is 'main.blg' -[36] biber:340> INFO - === Di Mai 5, 2026, 15:25:39 -[44] Biber.pm:419> INFO - Reading 'main.bcf' -[73] Biber.pm:979> INFO - Found 55 citekeys in bib section 0 -[79] Biber.pm:4419> INFO - Processing section 0 -[84] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0 -[86] bibtex.pm:1713> INFO - LaTeX decoding ... -[116] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib' -[298] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized' +[40] biber:340> INFO - === Do Mai 7, 2026, 17:17:44 +[48] Biber.pm:419> INFO - Reading 'main.bcf' +[77] Biber.pm:979> INFO - Found 55 citekeys in bib section 0 +[82] Biber.pm:4419> INFO - Processing section 0 +[87] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0 +[89] bibtex.pm:1713> INFO - LaTeX decoding ... +[119] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib' [298] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable' +[298] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized' [298] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US' [298] Biber.pm:4245> INFO - No sort tailoring available for locale 'en-US' -[323] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8' -[334] bbl.pm:763> INFO - Output to main.bbl -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 10, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 21, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 38, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 49, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 58, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 73, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 82, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 91, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 100, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 109, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 118, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 127, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 136, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 157, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 178, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 187, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 196, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 207, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 218, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 229, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 240, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 249, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 258, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 269, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 278, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 289, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 300, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 309, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 328, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 337, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 400, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 419, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 428, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 437, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 456, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 491, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 526, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 535, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 556, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 565, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 576, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 587, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 619, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 648, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 658, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 667, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 688, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 709, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 720, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 729, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 749, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 766, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 775, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 800, warning: 6 characters of junk seen at toplevel -[335] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_MSWW/347c261ec4135a5723bef5c751f5078f_23965.utf8, line 817, warning: 6 characters of junk seen at toplevel -[336] Biber.pm:133> INFO - WARNINGS: 55 +[322] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8' +[332] bbl.pm:763> INFO - Output to main.bbl +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 10, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 21, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 38, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 49, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 58, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 73, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 82, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 91, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 100, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 109, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 118, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 127, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 136, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 157, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 178, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 187, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 196, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 207, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 218, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 229, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 240, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 249, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 258, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 269, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 278, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 289, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 300, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 309, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 328, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 337, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 400, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 419, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 428, warning: 6 characters of junk seen at toplevel +[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 437, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 456, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 491, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 526, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 535, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 556, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 565, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 576, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 587, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 619, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 648, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 658, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 667, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 688, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 709, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 720, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 729, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 749, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 766, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 775, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 800, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 817, warning: 6 characters of junk seen at toplevel +[333] Biber.pm:133> INFO - WARNINGS: 55 diff --git a/main.fdb_latexmk b/main.fdb_latexmk index 00ea3ab..31be66b 100644 --- a/main.fdb_latexmk +++ b/main.fdb_latexmk @@ -1,14 +1,14 @@ # Fdb version 4 -["biber main"] 1777987539.56299 "main.bcf" "main.bbl" "main" 1777997863.65105 0 +["biber main"] 1778167064.11549 "main.bcf" "main.bbl" "main" 1778170419.19465 0 "cite.bib" 1770904753.08918 27483 4290db0c91f7b5055e25472ef913f6b4 "" - "main.bcf" 1777997863.5786 112931 2a478116d80ebb1ada7083a24facd6e3 "pdflatex" + "main.bcf" 1778170419.12045 112931 2a478116d80ebb1ada7083a24facd6e3 "pdflatex" (generated) "main.bbl" "main.blg" (rewritten before read) -["pdflatex"] 1777997862.51574 "/home/hartling/phd/paper/paper_2025/main.tex" "main.pdf" "main" 1777997863.65129 0 +["pdflatex"] 1778170418.0584 "/home/hartling/phd/paper/paper_2025/main.tex" "main.pdf" "main" 1778170419.19487 0 "/etc/texmf/web2c/texmf.cnf" 1761560044.43676 475 c0e671620eb5563b2130f56340a5fde8 "" - "/home/hartling/phd/paper/paper_2025/main.tex" 1777997862.37961 70656 c13f6515f4f23776571f53ea8d8e5ebf "" + "/home/hartling/phd/paper/paper_2025/main.tex" 1778170417.91947 85421 7d6468de7ebe30036ecfa040f5dc165b "" "/usr/share/texlive/texmf-dist/fonts/map/fontname/texfonts.map" 1577235249 3524 cb3e574dea2d1052e39280babc910dc8 "" "/usr/share/texlive/texmf-dist/fonts/tfm/public/amsfonts/cmextra/cmex7.tfm" 1246382020 1004 54797486969f23fa377b128694d548df "" "/usr/share/texlive/texmf-dist/fonts/tfm/public/amsfonts/cmextra/cmex8.tfm" 1246382020 988 bdf658c3bfc2d96d3c8b02cfc1c94c20 "" @@ -155,7 +155,7 @@ "/var/lib/texmf/web2c/pdftex/pdflatex.fmt" 1761648508 8213325 7fd20752ab46ff9aa583e4973d7433df "" "figures/fig_auditory_pathway.pdf" 1771593904.14638 1153923 3df8539421fd21dc866cc8d320bd9b1d "" "figures/fig_feat_stages.pdf" 1777568594.52063 11308299 aa000e352d557e9395028dd7235cf375 "" - "figures/fig_features_cross_species.pdf" 1777984976.24602 145978 207f2e0de08b5ff0465d12d6450563c3 "" + "figures/fig_features_cross_species.pdf" 1778167576.33731 206101 cd143cc5c7c8c6d10323b077f6d09274 "" "figures/fig_invariance_cross_species_thresh_appendix.pdf" 1777568946.883 302422 5f33b50142db8b69ae9735c4aa8be688 "" "figures/fig_invariance_field.pdf" 1776952657.04263 9131898 e9d9acff1d03fdf60ddc9e32b87ae6c2 "" "figures/fig_invariance_full_Omocestus_rufipes.pdf" 1777914919.48322 4817854 489437547b91e2c147d390725d05c6bd "" @@ -166,7 +166,7 @@ "figures/fig_invariance_short_Omocestus_rufipes.pdf" 1777915059.23603 1964460 d7d5094b99b7a2cfc755e60b9cc107ae "" "figures/fig_invariance_thresh-lp_noise_appendix.pdf" 1777378570.40754 1484986 42bec6aa96a984e1b2872daafbf7decf "" "figures/fig_invariance_thresh-lp_pure_appendix.pdf" 1777378581.6234 1387834 7815dfef418fdf540749fafb8a79ac6f "" - "figures/fig_invariance_thresh_lp_single.pdf" 1777997361.03636 857858 9624f0319932e6bb2d33b2aa5fda20bf "" + "figures/fig_invariance_thresh_lp_single.pdf" 1778054328.23672 857898 f85025e7a56d6a48d2c356ef77bf835a "" "figures/fig_invariance_thresh_lp_species.pdf" 1777378512.7163 1607791 f0b47f0ad73ff3b1dd65eee81fb5abfb "" "figures/fig_kernel_sd_perc_field_appendix.pdf" 1777273985.83211 100184 e699513599b5828cd498b1621e1e79ee "" "figures/fig_kernel_sd_perc_full_appendix.pdf" 1777273971.01214 90770 e3ecb7db816fc5046b866a9b27a35193 "" @@ -175,10 +175,10 @@ "figures/fig_noise_env_sd_conversion_appendix.pdf" 1776328774.43347 45466 c2be20312c1572203bdbeb9c8e32525e "" "figures/fig_pre_stages.pdf" 1777568592.25966 441645 7231cde61e83c7ce28a9e6cfaceac8d3 "" "figures/fig_saturation_log-hp_appendix.pdf" 1777378621.26288 28579 137855d03bab8dc5f6d31b70d404e082 "" - "main.aux" 1777997863.5716 24878 bfeaa2e15d3b650dd201213b3639cfd2 "pdflatex" - "main.bbl" 1777987540.19379 91039 1380dc8c93d2855fdb132cc5a40ad52f "biber main" - "main.run.xml" 1777997863.5796 2335 a049bc26a7f032e842ce55de5bc38328 "pdflatex" - "main.tex" 1777997862.37961 70656 c13f6515f4f23776571f53ea8d8e5ebf "" + "main.aux" 1778170419.11345 26035 768c445b960ba491486f92ae60247ec7 "pdflatex" + "main.bbl" 1778167064.78898 91039 1380dc8c93d2855fdb132cc5a40ad52f "biber main" + "main.run.xml" 1778170419.12045 2335 a049bc26a7f032e842ce55de5bc38328 "pdflatex" + "main.tex" 1778170417.91947 85421 7d6468de7ebe30036ecfa040f5dc165b "" (generated) "main.aux" "main.bcf" diff --git a/main.fls b/main.fls index 453b711..fb5478e 100644 --- a/main.fls +++ b/main.fls @@ -246,6 +246,7 @@ INPUT /usr/share/texlive/texmf-dist/fonts/tfm/public/cm/cmbx12.tfm INPUT /usr/share/texlive/texmf-dist/fonts/tfm/public/cm/cmti12.tfm OUTPUT main.pdf INPUT /var/lib/texmf/fonts/map/pdftex/updmap/pdftex.map +INPUT /usr/share/texlive/texmf-dist/fonts/tfm/public/cm/cmbx12.tfm INPUT ./figures/fig_auditory_pathway.pdf INPUT ./figures/fig_auditory_pathway.pdf INPUT ./figures/fig_auditory_pathway.pdf @@ -254,7 +255,6 @@ INPUT ./figures/fig_auditory_pathway.pdf INPUT /usr/share/texlive/texmf-dist/fonts/tfm/public/cm/cmbx12.tfm INPUT /usr/share/texlive/texmf-dist/fonts/tfm/public/cm/cmr10.tfm INPUT /usr/share/texlive/texmf-dist/fonts/tfm/public/cm/cmbx10.tfm -INPUT /usr/share/texlive/texmf-dist/fonts/tfm/public/cm/cmbx12.tfm INPUT /usr/share/texlive/texmf-dist/fonts/tfm/public/cm/cmr8.tfm INPUT /usr/share/texlive/texmf-dist/fonts/tfm/public/cm/cmr6.tfm INPUT /usr/share/texlive/texmf-dist/fonts/tfm/public/cm/cmmi12.tfm @@ -318,17 +318,17 @@ INPUT ./figures/fig_invariance_short_Omocestus_rufipes.pdf INPUT ./figures/fig_invariance_short_Omocestus_rufipes.pdf INPUT ./figures/fig_invariance_short_Omocestus_rufipes.pdf INPUT ./figures/fig_invariance_short_Omocestus_rufipes.pdf +INPUT ./figures/fig_invariance_field.pdf +INPUT ./figures/fig_invariance_field.pdf +INPUT ./figures/fig_invariance_field.pdf +INPUT ./figures/fig_invariance_field.pdf +INPUT ./figures/fig_invariance_field.pdf INPUT ./figures/fig_features_cross_species.pdf INPUT ./figures/fig_features_cross_species.pdf INPUT ./figures/fig_features_cross_species.pdf INPUT ./figures/fig_features_cross_species.pdf INPUT ./figures/fig_features_cross_species.pdf INPUT /usr/share/texlive/texmf-dist/fonts/tfm/public/cm/cmti10.tfm -INPUT ./figures/fig_invariance_field.pdf -INPUT ./figures/fig_invariance_field.pdf -INPUT ./figures/fig_invariance_field.pdf -INPUT ./figures/fig_invariance_field.pdf -INPUT ./figures/fig_invariance_field.pdf INPUT ./figures/fig_noise_env_sd_conversion_appendix.pdf INPUT ./figures/fig_noise_env_sd_conversion_appendix.pdf INPUT ./figures/fig_noise_env_sd_conversion_appendix.pdf diff --git a/main.log b/main.log index 7f99859..fb452b8 100644 --- a/main.log +++ b/main.log @@ -1,4 +1,4 @@ -This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023/Debian) (preloaded format=pdflatex 2025.10.28) 5 MAY 2026 18:17 +This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023/Debian) (preloaded format=pdflatex 2025.10.28) 7 MAY 2026 18:13 entering extended mode restricted \write18 enabled. file:line:error style messages enabled. @@ -702,29 +702,29 @@ LaTeX Font Info: Trying to load font information for U+msb on input line 41. (/usr/share/texlive/texmf-dist/tex/latex/amsfonts/umsb.fd File: umsb.fd 2013/01/14 v3.01 AMS symbols B ) -Overfull \hbox (54.40451pt too wide) in paragraph at lines 128--159 +Overfull \hbox (54.40451pt too wide) in paragraph at lines 129--160 \OT1/cmr/m/n/12 and eval-u-a-tion ([]), sender lo-cal-iza-tion ([]), [] -Overfull \hbox (9.21051pt too wide) in paragraph at lines 128--159 +Overfull \hbox (9.21051pt too wide) in paragraph at lines 129--160 \OT1/cmr/m/n/12 tion sig-nals for dif-fer-ent con-texts and ranges us-ing their wings, hindlegs, or mandibles ([]). [] [1 {/var/lib/texmf/fonts/map/pdftex/updmap/pdftex.map}] [2] -Overfull \hbox (42.86342pt too wide) in paragraph at lines 224--285 +Overfull \hbox (42.86342pt too wide) in paragraph at lines 225--286 \OT1/cmr/m/n/12 and grasshop-pers ([]; re-view on both: []). [] -Overfull \hbox (3.29253pt too wide) in paragraph at lines 224--285 +Overfull \hbox (3.29253pt too wide) in paragraph at lines 225--286 []\OT1/cmr/m/n/12 ; []). The fit-ted sig-moidal [] [3] -Overfull \hbox (41.1838pt too wide) in paragraph at lines 318--353 +Overfull \hbox (41.1838pt too wide) in paragraph at lines 321--356 \OT1/cmr/m/n/12 for con-spe-cific song recog-ni-tion and re-sponse ini-ti-a-tion ([]; [] @@ -732,10 +732,10 @@ Overfull \hbox (41.1838pt too wide) in paragraph at lines 318--353 File: figures/fig_auditory_pathway.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_auditory_pathway.pdf used on input line 356. +Package pdftex.def Info: figures/fig_auditory_pathway.pdf used on input line 359. (pdftex.def) Requested size: 483.69687pt x 518.2677pt. -Overfull \hbox (65.93214pt too wide) in paragraph at lines 378--384 +Overfull \hbox (65.93214pt too wide) in paragraph at lines 381--387 \OT1/cmr/m/n/12 tym-pa-nal mem-brane acts as a me-chan-i-cal res-o-nance fil-ter for sound-induced vi-bra-tions ([]; [] @@ -750,10 +750,10 @@ Overfull \vbox (0.8319pt too high) has occurred while \output is active [] File: figures/fig_pre_stages.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_pre_stages.pdf used on input line 427. +Package pdftex.def Info: figures/fig_pre_stages.pdf used on input line 430. (pdftex.def) Requested size: 483.69687pt x 241.84782pt. [7] [8 <./figures/fig_pre_stages.pdf>] -Overfull \hbox (42.89445pt too wide) in paragraph at lines 521--530 +Overfull \hbox (42.89445pt too wide) in paragraph at lines 524--533 \OT1/cmr/m/n/12 glion ([]; []; []). [] @@ -761,19 +761,19 @@ Overfull \hbox (42.89445pt too wide) in paragraph at lines 521--530 File: figures/fig_feat_stages.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_feat_stages.pdf used on input line 542. +Package pdftex.def Info: figures/fig_feat_stages.pdf used on input line 545. (pdftex.def) Requested size: 483.69687pt x 241.84782pt. [10 <./figures/fig_feat_stages.pdf>] [11] File: figures/fig_invariance_rect_lp.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_invariance_rect_lp.pdf used on input line 626. +Package pdftex.def Info: figures/fig_invariance_rect_lp.pdf used on input line 633. (pdftex.def) Requested size: 483.69687pt x 310.80379pt. [12 <./figures/fig_invariance_rect_lp.pdf>] [13] File: figures/fig_invariance_log_hp.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_invariance_log_hp.pdf used on input line 765. +Package pdftex.def Info: figures/fig_invariance_log_hp.pdf used on input line 772. (pdftex.def) Requested size: 483.69687pt x 483.85846pt. [14] @@ -783,106 +783,118 @@ LaTeX Warning: Text page 15 contains only floats. File: figures/fig_invariance_thresh_lp_single.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_invariance_thresh_lp_single.pdf used on input line 901. +Package pdftex.def Info: figures/fig_invariance_thresh_lp_single.pdf used on input line 908. (pdftex.def) Requested size: 483.69687pt x 483.69566pt. - [17] [18 <./figures/fig_invariance_thresh_lp_single.pdf>] - + [17] [18 <./figures/fig_invariance_thresh_lp_single.pdf>] [19 + +] + File: figures/fig_invariance_thresh_lp_species.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_invariance_thresh_lp_species.pdf used on input line 939. +Package pdftex.def Info: figures/fig_invariance_thresh_lp_species.pdf used on input line 1012. (pdftex.def) Requested size: 483.69687pt x 483.69566pt. - + [20] + +LaTeX Warning: Text page 21 contains only floats. + +[21 <./figures/fig_invariance_thresh_lp_species.pdf>] [22] + File: figures/fig_invariance_full_Omocestus_rufipes.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_invariance_full_Omocestus_rufipes.pdf used on input line 995. +Package pdftex.def Info: figures/fig_invariance_full_Omocestus_rufipes.pdf used on input line 1121. (pdftex.def) Requested size: 483.69687pt x 483.69566pt. - [19 - - <./figures/fig_invariance_thresh_lp_species.pdf>] [20 <./figures/fig_invariance_full_Omocestus_rufipes.pdf>] - + [23] [24 <./figures/fig_invariance_full_Omocestus_rufipes.pdf>] + File: figures/fig_invariance_short_Omocestus_rufipes.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_invariance_short_Omocestus_rufipes.pdf used on input line 1033. +Package pdftex.def Info: figures/fig_invariance_short_Omocestus_rufipes.pdf used on input line 1203. (pdftex.def) Requested size: 483.69687pt x 483.69566pt. - [21 <./figures/fig_invariance_short_Omocestus_rufipes.pdf>] - -File: figures/fig_features_cross_species.pdf Graphic file (type pdf) - -Package pdftex.def Info: figures/fig_features_cross_species.pdf used on input line 1070. -(pdftex.def) Requested size: 483.69687pt x 483.69566pt. - + [25 + +] [26 <./figures/fig_invariance_short_Omocestus_rufipes.pdf>] + File: figures/fig_invariance_field.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_invariance_field.pdf used on input line 1100. +Package pdftex.def Info: figures/fig_invariance_field.pdf used on input line 1242. (pdftex.def) Requested size: 483.69687pt x 483.69566pt. - [22 + [27 + <./figures/fig_invariance_field.pdf>] + +File: figures/fig_features_cross_species.pdf Graphic file (type pdf) + +Package pdftex.def Info: figures/fig_features_cross_species.pdf used on input line 1295. +(pdftex.def) Requested size: 483.69687pt x 483.69566pt. + [28] - <./figures/fig_features_cross_species.pdf>] [23 <./figures/fig_invariance_field.pdf>] +LaTeX Warning: Text page 29 contains only floats. -LaTeX Warning: Reference `eq:toy_snr' on page 24 undefined on input line 1137. +[29 <./figures/fig_features_cross_species.pdf>] -[24] - +LaTeX Warning: Reference `eq:toy_snr' on page 30 undefined on input line 1352. + +[30] + File: figures/fig_noise_env_sd_conversion_appendix.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_noise_env_sd_conversion_appendix.pdf used on input line 1169. +Package pdftex.def Info: figures/fig_noise_env_sd_conversion_appendix.pdf used on input line 1384. (pdftex.def) Requested size: 483.69687pt x 241.84782pt. - + File: figures/fig_invariance_rect-lp_appendix.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_invariance_rect-lp_appendix.pdf used on input line 1178. +Package pdftex.def Info: figures/fig_invariance_rect-lp_appendix.pdf used on input line 1393. (pdftex.def) Requested size: 483.69687pt x 241.84782pt. - + [31 <./figures/fig_noise_env_sd_conversion_appendix.pdf>] + File: figures/fig_invariance_log-hp_appendix.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_invariance_log-hp_appendix.pdf used on input line 1187. +Package pdftex.def Info: figures/fig_invariance_log-hp_appendix.pdf used on input line 1402. (pdftex.def) Requested size: 483.69687pt x 241.84782pt. - [25 <./figures/fig_noise_env_sd_conversion_appendix.pdf> <./figures/fig_invariance_rect-lp_appendix.pdf>] - + File: figures/fig_saturation_log-hp_appendix.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_saturation_log-hp_appendix.pdf used on input line 1196. +Package pdftex.def Info: figures/fig_saturation_log-hp_appendix.pdf used on input line 1411. (pdftex.def) Requested size: 483.69687pt x 241.84782pt. - + [32 <./figures/fig_invariance_rect-lp_appendix.pdf> <./figures/fig_invariance_log-hp_appendix.pdf>] + File: figures/fig_invariance_thresh-lp_pure_appendix.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_invariance_thresh-lp_pure_appendix.pdf used on input line 1205. +Package pdftex.def Info: figures/fig_invariance_thresh-lp_pure_appendix.pdf used on input line 1420. (pdftex.def) Requested size: 483.69687pt x 241.84782pt. - [26 <./figures/fig_invariance_log-hp_appendix.pdf> <./figures/fig_saturation_log-hp_appendix.pdf>] - + File: figures/fig_invariance_thresh-lp_noise_appendix.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_invariance_thresh-lp_noise_appendix.pdf used on input line 1214. +Package pdftex.def Info: figures/fig_invariance_thresh-lp_noise_appendix.pdf used on input line 1429. (pdftex.def) Requested size: 483.69687pt x 241.84782pt. - + [33 <./figures/fig_saturation_log-hp_appendix.pdf> <./figures/fig_invariance_thresh-lp_pure_appendix.pdf>] + File: figures/fig_kernel_sd_perc_thresh_lp_appendix.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_kernel_sd_perc_thresh_lp_appendix.pdf used on input line 1223. +Package pdftex.def Info: figures/fig_kernel_sd_perc_thresh_lp_appendix.pdf used on input line 1438. (pdftex.def) Requested size: 483.69687pt x 241.84782pt. - [27 <./figures/fig_invariance_thresh-lp_pure_appendix.pdf> <./figures/fig_invariance_thresh-lp_noise_appendix.pdf>] - + File: figures/fig_kernel_sd_perc_full_appendix.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_kernel_sd_perc_full_appendix.pdf used on input line 1232. +Package pdftex.def Info: figures/fig_kernel_sd_perc_full_appendix.pdf used on input line 1447. (pdftex.def) Requested size: 483.69687pt x 241.84782pt. - + [34 <./figures/fig_invariance_thresh-lp_noise_appendix.pdf> <./figures/fig_kernel_sd_perc_thresh_lp_appendix.pdf>] + File: figures/fig_kernel_sd_perc_short_appendix.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_kernel_sd_perc_short_appendix.pdf used on input line 1241. +Package pdftex.def Info: figures/fig_kernel_sd_perc_short_appendix.pdf used on input line 1456. (pdftex.def) Requested size: 483.69687pt x 241.84782pt. - [28 <./figures/fig_kernel_sd_perc_thresh_lp_appendix.pdf> <./figures/fig_kernel_sd_perc_full_appendix.pdf>] - + File: figures/fig_kernel_sd_perc_field_appendix.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_kernel_sd_perc_field_appendix.pdf used on input line 1250. +Package pdftex.def Info: figures/fig_kernel_sd_perc_field_appendix.pdf used on input line 1465. (pdftex.def) Requested size: 483.69687pt x 241.84782pt. - + [35 <./figures/fig_kernel_sd_perc_full_appendix.pdf> <./figures/fig_kernel_sd_perc_short_appendix.pdf>] + File: figures/fig_invariance_cross_species_thresh_appendix.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_invariance_cross_species_thresh_appendix.pdf used on input line 1260. +Package pdftex.def Info: figures/fig_invariance_cross_species_thresh_appendix.pdf used on input line 1475. (pdftex.def) Requested size: 483.69687pt x 483.69566pt. - [29 <./figures/fig_kernel_sd_perc_short_appendix.pdf> <./figures/fig_kernel_sd_perc_field_appendix.pdf>] [30 <./figures/fig_invariance_cross_species_thresh_appendix.pdf>] (./main.aux) + [36 <./figures/fig_kernel_sd_perc_field_appendix.pdf>] [37 <./figures/fig_invariance_cross_species_thresh_appendix.pdf>] (./main.aux) *********** LaTeX2e <2023-11-01> patch level 1 L3 programming layer <2024-01-22> @@ -896,18 +908,18 @@ Package logreq Info: Writing requests to 'main.run.xml'. ) Here is how much of TeX's memory you used: - 20879 strings out of 474222 - 453981 string characters out of 5748732 + 20880 strings out of 474222 + 453984 string characters out of 5748732 1937975 words of memory out of 5000000 - 42868 multiletter control sequences out of 15000+600000 + 42869 multiletter control sequences out of 15000+600000 569740 words of font info for 80 fonts, out of 8000000 for 9000 1143 hyphenation exceptions out of 8191 94i,18n,93p,1751b,1738s stack positions out of 10000i,1000n,20000p,200000b,200000s -Output written on main.pdf (30 pages, 39171711 bytes). +Output written on main.pdf (37 pages, 39250039 bytes). PDF statistics: - 2556 PDF objects out of 2984 (max. 8388607) - 1129 compressed objects within 12 object streams + 2584 PDF objects out of 2984 (max. 8388607) + 1147 compressed objects within 12 object streams 0 named destinations out of 1000 (max. 500000) 123 words of extra memory for PDF output out of 10000 (max. 10000000) diff --git a/main.pdf b/main.pdf index 27478e9..8922651 100644 Binary files a/main.pdf and b/main.pdf differ diff --git a/main.synctex.gz b/main.synctex.gz index 7a2de5f..0f307e8 100644 Binary files a/main.synctex.gz and b/main.synctex.gz differ diff --git a/main.tex b/main.tex index e2f0f9c..87ebf1b 100644 --- a/main.tex +++ b/main.tex @@ -105,6 +105,7 @@ \newcommand{\nvar}{\sigma_{\eta}^{2}} % Noise component variance \newcommand{\pc}{p(c,\,T)} % Probability density (general interval) \newcommand{\pclp}{p(c,\,\tlp)} % Probability density (lowpass interval) +\newcommand{\muf}{\mu_{f_i}} % Average feature value \section{Exploring a grasshopper's sensory world} @@ -312,7 +313,9 @@ within the auditory pathway. % - How to integrate the available knowledge on anatomy, physiology, ethology?\\ % $\rightarrow$ Abstract, simplify, formalize $\rightarrow$ Functional model framework -\section{Developing a functional model of the\\grasshopper song recognition pathway} +\section{Methods} + +\subsection{Functional model of the grasshopper song recognition pathway} % Too long (no splitting, only pruning). The essence of constructing a functional model of a given system is to gain a @@ -373,7 +376,7 @@ outlined in the following sections. \label{fig:pathway} \end{figure} -\subsection{Population-driven signal preprocessing} +\subsubsection{Population-driven signal preprocessing} Grasshoppers receive airborne sound waves by a tympanal organ at either side of the body. The tympanal membrane acts as a mechanical resonance filter for @@ -436,7 +439,7 @@ following feature extraction stage. \end{figure} \FloatBarrier -\subsection{Feature extraction by individual neurons} +\subsubsection{Feature extraction by individual neurons} The ascending neurons extract and encode a number of different features of the preprocessed signal. As a population, they hence represent the signal in a @@ -555,7 +558,11 @@ can be read out by a simple linear classifier. \end{figure} \FloatBarrier -\section{Mechanisms driving the emergence of\\intensity-invariant song representation} +\subsubsection{Simulation-based analysis of the model pathway} + +\section{Results} + +\subsection{Mechanisms driving the emergence of intensity invariance} % Still missing the SNR analysis. Should be able to write around it for now. The robustness of song recognition is tied to the degree of intensity @@ -571,7 +578,7 @@ intensity variations. The two mechanisms each comprise a nonlinear signal transformation followed by a linear signal transformation but differ in the specific operations involved, as outlined in the following sections. -\subsection{Full-wave rectification \& lowpass filtering} +\subsubsection{Full-wave rectification \& lowpass filtering} The first nonlinear transformation along the model pathway is the full-wave rectification of the tympanal signal $\filt(t)$ during the extraction of the @@ -651,7 +658,7 @@ more robust input representation and higher input SNR. \end{figure} \FloatBarrier -\subsection{Logarithmic compression \& spike-frequency adaptation} +\subsubsection{Logarithmic compression \& spike-frequency adaptation} The second nonlinear transformation along the model pathway is the logarithmic compression of the signal envelope $\env(t)$ into $\db(t)$, Eq.\,\ref{eq:log}, @@ -794,7 +801,7 @@ is a recurring phenomenon that is further addressed in the following sections. \end{figure} \FloatBarrier -\subsection{Thresholding nonlinearity \& temporal averaging} +\subsubsection{Thresholding nonlinearity \& temporal averaging} The third nonlinear transformation along the model pathway is the thresholding nonlinearity $\nl$ that transforms each kernel response $c_i(t)$ into a binary @@ -809,13 +816,13 @@ rescaled~(Fig.\,\ref{fig:thresh-lp_single}a) and convolved with kernel $k(t)$. The resulting kernel response $c(t)$ was passed through $H(c\,-\,\Theta)$ with three different threshold values $\Theta$~(Fig.\,\ref{fig:thresh-lp_single}b-d). Each resulting binary response -$b(t)$ was transformed into $f(t)$, whose average feature value serves as a -measure of intensity~(Fig.\,\ref{fig:thresh-lp_single}ef). The thresholding -nonlinearity $H(c\,-\,\Theta)$ categorizes the values of $c(t)$ into "relevant" -($c(t)>\Theta$, $b(t)=1$) and "irrelevant" ($c(t)\leq\Theta$, $b(t)=0$) -response values. It thereby splits the probability density $\pc$ of $c(t)$ -within some observed time interval $T$ into two complementary parts around -$\Theta$: +$b(t)$ was transformed into $f(t)$, whose average feature value $\mu_f$ serves +as a measure of intensity~(Fig.\,\ref{fig:thresh-lp_single}ef). The +thresholding nonlinearity $H(c\,-\,\Theta)$ categorizes the values of $c(t)$ +into "relevant" ($c(t)>\Theta$, $b(t)=1$) and "irrelevant" ($c(t)\leq\Theta$, +$b(t)=0$) response values. It thereby splits the probability density $\pc$ of +$c(t)$ within some observed time interval $T$ into two complementary parts +around $\Theta$: \begin{equation} \int_{\Theta}^{+\infty} \pc\,dc\,=\,1\,-\,\int_{-\infty}^{\Theta} \pc\,dc\,=\,\frac{T_1}{T}, \qquad \infint \pc\,dc\,=\,1 \label{eq:pdf_split} @@ -856,45 +863,45 @@ points at which $c(t)$ crosses $\Theta$: The steeper the slope of $c(t)$, the less $T_1$ changes with variations in $\sca$. The most reliable way of exploiting this invariant porperty of $f(t)$ is to set $\Theta$ to a value near 0, because these values are least affected by different scales of $c(t)$. For -sufficiently large $\sca$, $f(t)$ then approaches the same constant value in +sufficiently large $\sca$, $f(t)$ then approaches the same constant $\mu_f$ in both the noiseless and the noisy case~(Fig.\,\ref{fig:thresh-lp_single}e, saturation regime). -The value of $f(t)$ in the saturation regime is independent of the precise +The value of $\mu_f$ in the saturation regime is independent of the precise value of $\Theta$, but the value of $\sca$ at which the saturation regime is reached decreses with $\Theta$~(Fig.\,\ref{fig:thresh-lp_single}e). Therefore, a threshold value of $\Theta=0$ would be the optimal choice for achieving intensity invariance at the lowest possible $\sca$. In stark contrast, the -closer $\Theta$ is to 0, the higher the pure-noise response of $f(t)$ and the -lower the resulting SNR of $f(t)$ between noise regime and saturation -regime~(Fig.\,\ref{fig:thresh-lp_single}b-d, left column, and -Fig.\,\ref{fig:thresh-lp_single}e). It is even possible to achieve an +closer $\Theta$ is to 0, the higher $\mu_f$ in response to the pure noise +component $\noc(t)$ and the lower the resulting SNR of $f(t)$ between noise +regime and saturation regime~(Fig.\,\ref{fig:thresh-lp_single}b-d, left column, +and Fig.\,\ref{fig:thresh-lp_single}e). It is even possible to achieve an "unlimited" SNR of $f(t)$ by setting $\Theta$ above the maximum of the -pure-noise $c(t)$, so that any value of $f(t)$ greater than 0 indicates the -presence of the song component $\soc(t)$ in input $\adapt(t)$ at the cost of -requiring a higher $\sca$ to reach the saturation regime. This trade-off -between intensity invariance and SNR has already been observed during the -previous analysis on logarithmic compression and -adaptation~(Fig.\,\ref{fig:log-hp}d). However, the parameters that determine -the SNR of $\adapt(t)$ are much less understood and likely relate to properties -of the signal, whereas the SNR of $f(t)$ depends on the choice of $\Theta$ and -can be more directly manipulated by the system. +pure-noise $c(t)$, so that any $\mu_f>0$ indicates the presence of the song +component $\soc(t)$ in input $\adapt(t)$ at the cost of requiring a higher +$\sca$ to reach the saturation regime. This trade-off between intensity +invariance and SNR has already been observed during the previous analysis on +logarithmic compression and adaptation~(Fig.\,\ref{fig:log-hp}d). However, the +parameters that determine the SNR of $\adapt(t)$ are much less understood and +likely relate to properties of the signal, whereas the SNR of $f(t)$ depends on +the choice of $\Theta$ and can be more directly manipulated by the system. Finally, the effects of thresholding and temporal averaging must be seen in the context of the previous transformation pair of logarithmic compression and -adaptation. - -Finally, the question remains whether the intensity-invariant output $\adapt(t)$ -of the previous transformation pair allows feature - -Finally, the output $\adapt(t)$ of the previous transformation -pair~(Fig.\,\ref{fig:log-hp}cd) can be related to the input $\adapt(t)$ of the -current transformation pair by plotting the values of $f(t)$ over the standard -deviation of input $\adapt(t)$ instead of -$\sca$~(Fig.\,\ref{fig:thresh-lp_single}f). This is relevant because, unlike -$\sca$, the standard deviation of $\adapt(t)$ is capped to a maximum value of -around 10\,dB by the previous transformation pair~(Fig.\,\ref{fig:log-hp}cd) - +adaptation: In the current analysis, the input $\adapt(t)$ can be rescaled by +arbitrarily large $\sca$, while in the full pathway, the current input +$\adapt(t)$ is the output $\adapt(t)$ of the previous transformation pair and +is hence capped to a maximum standard deviation of around +10\,dB~(Fig.\,\ref{fig:log-hp}cd). This can be illustrated by plotting $\mu_f$ +not over $\sca$~(Fig.\,\ref{fig:thresh-lp_single}e) but over the standard +deviation of input $\adapt(t)$ instead~(Fig.\,\ref{fig:thresh-lp_single}f). It +becomes apparent that $\mu_f$ saturates only for standard deviations of +$\adapt(t)$ that would already be capped. Accordingly, $f(t)$ never reaches the +saturation regime as determined by the current transformation pair but rather +adheres to the saturation regime determined by the previous transformation +pair. In this case, the saturated $\mu_f$ is not independent of $\Theta$ +anymore. The consequences of this interaction between the two mechanisms of +intensity invariance are further explored in a later section. \begin{figure}[!ht] \centering @@ -934,6 +941,72 @@ around 10\,dB by the previous transformation pair~(Fig.\,\ref{fig:log-hp}cd) \end{figure} \FloatBarrier +\subsection{Intensity invariance of species-specific feature representations} + +Having established both the meaning of the feature value and the mechanism of +intensity invariance by thresholding and temporal averaging, the question +remains how this mechanism acts on a set of features $f_i(t)$ based on +different species-specific songs~(Fig.\,\ref{fig:thresh-lp_species}a). The +previous analysis was repeated with three different kernels $k_i(t)$ using a +single kernel-specific threshold value $\thr$; and the resulting average +feature values $\muf$ were plotted over +$\sca$~(Fig.\,\ref{fig:thresh-lp_species}bc). Additionally, 2D feature spaces +spanned by each pair of $f_i(t)$ were plotted to investigate the separability +of species-specific songs based on the feature representation in dependence of +$\sca$~(Fig.\,\ref{fig:thresh-lp_species}de). Each species-specific combination +of $\muf$ follows a trajectory through feature space that develops with $\sca$. +These trajectories correspond to the transient regime between the constant +(noise) regime and the saturation regime, which are only visible as the start +and end points of the trajectories, respectively. The horizontal dashes in the +colorbars indicate the range of $\sca$ that corresponds to the transient regime +across $f_i(t)$ for each species. + +In the noiseless case, each $\muf$ is 0 for small $\sca$ across all +species~(Fig.\,\ref{fig:thresh-lp_species}b) because $c_i(t)$ never exceeds +$\thr$. Accordingly, each trajectory starts at the origin of the feature +space~(Fig.\,\ref{fig:thresh-lp_species}d). For larger $\sca$, all $\muf$ +saturate at individual values whose combination differs between species, so +that the songs of each species are eventually represented by distinct points in +feature space. However, the species-specific trajectories cross each other at +numerous points, which means that the songs of two species --- each at a +specific $\sca$ --- can result in the same combination of $\muf$. Furthermore, +the specific value of $\sca$ at which $\muf$ saturates depends on $f_i(t)$ and +the species: For \textit{C. mollis}, all $\muf$ saturate around the same +$\sca$, while \textit{O. rufipes} exhibits considerable variation between the +three $f_i(t)$. The larger the variation in saturation points between $f_i(t)$, +the stronger the curvature of the trajectory through feature space. + +In the noisy case, $\muf$ is non-zero even for the smallest +$\sca$~(Fig.\,\ref{fig:thresh-lp_species}c) because the addition of the noise +component $\noc(t)$ to input $\adapt(t)$ drives $c_i(t)$ above $\thr$ +regardless of the song component $\soc(t)$. The starting value of $\muf$ is the +same across all $f_i(t)$ and species by construction of the specific $\thr$. In +consequence, the trajectories through feature space do not start at the origin +but rather at approximately the same point along the +diagonal~(Fig.\,\ref{fig:thresh-lp_species}e). For larger $\sca$, all $\muf$ +saturate at the same values as in the noiseless case, as expected from the +previous analysis~(Fig.\,\ref{fig:thresh-lp_single}e). However, the +trajectories now move a much shorter distance through feature space for a +similar range of $\sca$ due to the lower SNR of $f_i(t)$ between noise regime +and saturation regime, which increases the likelihood of trajectories crossing +each other. Finally, the values of $\sca$ at which $\muf$ saturate for a given +species are slightly higher in the noisy case, but the variation between +$f_i(t)$ remains largely unchanged. + +In summary, even a comparably small set of three features $f_i(t)$ can, in +principle, represent different species-specific songs at distinct points in +feature space, regardless of the presence of noise. However, this only holds +for sufficiently large $\sca$ that allow $f_i(t)$ to reach a saturation regime. +During the transient regime, the species-specific combination of $\muf$ can +very well be the same for two or more different species at specific $\sca$, +although this may be alleviated by the inclusion of additional $f_i(t)$. +Overall, the results of this analysis suggest that $\thr$ should rather be +choosen in favor of a higher SNR ($\thr$ just above pure-noise $c_i(t)$) than a +lower saturation point ($\thr\to0$). First, because this reduces the density of +trajectories through feature space, and second, because the capping of +$\adapt(t)$ by the previous transformation pair likely renders the saturation +point of $f_i(t)$ less relevant. + \begin{figure}[!ht] \centering \includegraphics[width=\textwidth]{figures/fig_invariance_thresh_lp_species.pdf} @@ -968,28 +1041,81 @@ around 10\,dB by the previous transformation pair~(Fig.\,\ref{fig:log-hp}cd) \label{fig:thresh-lp_species} \end{figure} \FloatBarrier -% \caption{\textbf{Rectification and lowpass filtering improves SNR -% but does not contribute to intensity invariance.} -% Input $\raw(t)$ consists of song component $\soc(t)$ scaled by -% $\sca$ with optional noise component $\noc(t)$ and is -% successively transformed into tympanal signal $\filt(t)$ and -% envelope $\env(t)$. Different line styles indicate different -% cutoff frequencies $\fc$ of the lowpass filter extracting -% $\env(t)$. -% \textbf{Top}:~Example representations of $\filt(t)$ and -% $\env(t)$ for different $\sca$. -% \textbf{a}:~Noiseless case. -% \textbf{b}:~Noisy case. -% \textbf{Bottom}:~Intensity metrics over a range of $\sca$. -% \textbf{c}:~Noiseless case: Standard deviations of $\filt(t)$ -% and $\env(t)$. -% \textbf{d}:~Noisy case: Ratios of standard deviations of -% $\filt(t)$ and $\env(t)$ to the respective reference standard -% deviation for input $\raw(t)=\noc(t)$. -% \textbf{e}:~Ratios of standard deviations of $\env(t)$ as in -% \textbf{b} for different species (averaged over songs and -% recordings, see appendix Fig.\,\ref{fig:app_rect-lp}). -% } + +\subsection{Intensity invariance along the full model pathway} + +Through the previous analyses, we could establish two mechanisms of intensity +invariance: Logarithmic compression and adaptation as well as thresholding and +temporal averaging. While each transformation pair by itself can provide some +level of invariance, certain results suggest that the first mechanism may +actually limit or even nullify the effect of the second mechanism. In the +following sections, we investigate the combined effect of both mechanisms along +the full model pathway~(Fig.\,\ref{fig:pipeline_full}) and explore the +consequences of disabling the first mechanism by skipping the logarithmic +compression step~(Fig.\,\ref{fig:pipeline_short}). + +\subsubsection{Including logarithmic compression} + +For this analysis, input $\raw(t)$ --- including both song component $\soc(t)$ +and noise component $\noc(t)$ --- was rescaled and processed throughout all +steps of the model pathway~(Fig.\,\ref{fig:pipeline_full}a) up to the feature +set $f_i(t)$. As before, the standard deviation was used as intensity metric +for each resulting representation except $b_i(t)$ and $f_i(t)$. For $f_i(t)$, +the average feature value $\muf$ was used, while $b_i(t)$ was omitted from the +analysis. Plotting each intensity metric over +$\sca$~(Fig.\,\ref{fig:pipeline_full}b) reinforces many of the previous +observations. For ease of visualization, the kernel-specific curves for +$c_i(t)$ and $f_i(t)$ were summarized by their median. Representations prior to +logarithmic compression --- $\filt(t)$ and $\env(t)$ --- show a linear increase +of the intensity metric for larger $\sca$ on a double-logarithmic scale. +Representations after logarithmic compression --- $\db(t)$, $\adapt(t)$, and +$c_i(t)$ --- are the first to reach a saturation regime and do so at +approximately the same $\sca$ because they are separated only by linear +transformations. Feature set $f_i(t)$ reaches a saturation regime, as well. But +contrary to previous results, the saturation point of $f_i(t)$ appears below +that of $c_i(t)$, which suggests that the second mechanism of thresholding and +temporal averaging can indeed improve intensity invariance beyond the first +mechanism of logarithmic compression and adaptation. The difference in +saturation points is best illustrated based on the ratio of each intensity +metric to the respective pure-noise reference +value~(Fig.\,\ref{fig:pipeline_full}d). However, compressing $f_i(t)$ into a +median across $k_i(t)$ conceils many kernel-specific details. It is therefore +necessary to consider the development of each $f_i(t)$ over $\sca$ +separately~(Fig.\,\ref{fig:pipeline_full}c). Indeed, all 40 $f_i(t)$ in the set +reach a saturation regime for sufficiently large $\sca$. The saturated $\muf$ +are distributed over a range of values --- which is the prerequisite for +forming species-specific combinations --- but are limited to a rather small +subset of possible values between 0 and 1. Based on previous +results~(Fig.\,\ref{fig:thresh-lp_single}f), this is likely due to the capping +of $\adapt(t)$ that prevents $f_i(t)$ from reaching its intrinsic saturation +value; but this cannot be confirmed until the following +analysis~(Fig.\,\ref{fig:pipeline_short}). Looking at the kernel-specific SNR +values of $c_i(t)$ over $\sca$~(Fig.\,\ref{fig:pipeline_full}e) and $f_i(t)$ +over $\sca$~(Fig.\,\ref{fig:pipeline_full}f) reveals a high degree of variation +between different $k_i(t)$. Certain $f_i(t)$ achieve much higher SNR values +than $c_i(t)$ for the same $\sca$ due to the former's capacity for arbitrarily +low pure-noise responses ($\muf\to0$) and hence arbitrarily high SNR values. +Finally, the question remains whether the suspected improvement of intensity +invariance by $f_i(t)$ beyond $c_i(t)$ holds at the level of individual +$k_i(t)$. The single saturation points based on the median across $k_i(t)$ for +$c_i(t)$ and $f_i(t)$ are expanded into distributions of kernel-specific +saturation points~(Fig.\,\ref{fig:pipeline_full}g). For $c_i(t)$, the +distribution is rather narrow and corresponds well to the single saturation +point based on the median. For $f_i(t)$, however, the distribution is much +broader and is not centered around the single saturation point based on the +median but rather shifted towards lower $\sca$. Care must be taken when +interpreting the height of either distribution due to the logarithmic scaling +of the underlying $\sca$ axis. Nevertheless, the overall pattern suggests that +specific $f_i(t)$ can reach a saturation regime at lower $\sca$ than their +$c_i(t)$ counterparts. Therefore, the effect of thresholding and temporal +averaging on intensity invariance is not necessarily nullified by the previous +logarithmic compression and adaptation, which means that both mechanisms can, +in principle, work together towards an intensity-invariant song representation. +% Or does one simply overwrite the other? Can there even be a higher intensity +% invariance based on the sum of both effects? Or does one simply kick in for +% lower scales than the other and thus dictates the overall intensity +% invariance? Whatever, discussion material. + \begin{figure}[!ht] \centering \includegraphics[width=\textwidth]{figures/fig_invariance_full_Omocestus_rufipes.pdf} @@ -1028,6 +1154,50 @@ around 10\,dB by the previous transformation pair~(Fig.\,\ref{fig:log-hp}cd) \end{figure} \FloatBarrier +\subsubsection{Excluding logarithmic compression} + +The previous analysis was repeated in exactly the same way as before, except +that the logarithmic compression of $\env(t)$, Eq.\,\ref{eq:log}, was skipped +in order to disable the first mechanism of intensity invariance. Consequently, +$\adapt(t)$ is merely a highpass filtered version of $\env(t)$; and $\db(t)$ is +missing entirely~(Fig.\,\ref{fig:pipeline_short}a). As expected, all +representations prior to the thresholding nonlinearity $\nl$ --- $\filt(t)$, +$\env(t)$, $\adapt(t)$, and $c_i(t)$ --- show a linear increase of the +intensity metric for larger $\sca$, while $f_i(t)$ is the only representation +to reach a saturation regime~(Fig.\,\ref{fig:pipeline_short}bd). The +saturated $\muf$ are distributed over a much broader range of values than in +the previous analysis~(Fig.\,\ref{fig:pipeline_short}c). Intriguingly, the +distribution of $\muf$ is symmetric around a value of 0.5. This is relevant +because every kernel $k^+(t)$ in the underlying kernel set has a counterpart of +opposite sign that is otherwise identical, so that $k^+(t)=-k^-(t)$. The +responses of $k^+(t)$ and $k^-(t)$ to the same input $\adapt(t)$ are also +inverted because convolution is a linear operation: $c^+(t)=-c^-(t)$. The +distributions of $c^+(t)$ and $c^-(t)$ are hence inverted to each other, as +well: $p(c^+)=p(-c^-)$. Based on Eq.\,\ref{eq:feat_prop}, transforming $c^+(t)$ +and $c^-(t)$ further using the same $\Theta$ thus results in two complementary +features $f^+(t)$ and $f^-(t)$ that are symmetric around 0.5, so that +$f^+(t)=1-f^-(t)$. Of course, this symmetry throughout the feature +representation goes hand in hand with a substantial degree of redundancy and is +hardly expected to be present in the actual grasshopper auditory system. But +the fact that the saturated $\muf$ are distributed symmetrically around 0.5 +provides concrete evidence that each $f_i(t)$ is able to reach its intrinsic +saturation value in the absence of logarithmic +compression~(Fig.\,\ref{fig:pipeline_short}c), which is otherwise prevented by +the capping of $\adapt(t)$, as seen during previous +analyses~(Fig.\,\ref{fig:thresh-lp_single}f and +Fig.\,\ref{fig:pipeline_full}c). Otherwise, there appear to be no major +differences in the development of $f_i(t)$ over $\sca$ compared to the previous +analysis, neither on the kernel-specific SNR +values~(Fig.\,\ref{fig:pipeline_short}e) nor on the distribution of +kernel-specific saturation points~(Fig.\,\ref{fig:pipeline_short}f). Overall, +the most substantial consequence of skipping the logarithmic compression is +that it allows $f_i(t)$ to reach its intrinsic saturation value. If this +results in a wider range of $\muf$ across the feature set, it should be +benefitial for forming species-specific combinations. However, this depends on +multiple different factors such as the choice of $k_i(t)$ and $\thr$ as well as +the structure and distribution of the specific song and is hence not +guaranteed simply by disabling logarithmic compression. + \begin{figure}[!ht] \centering \includegraphics[width=\textwidth]{figures/fig_invariance_short_Omocestus_rufipes.pdf} @@ -1065,6 +1235,61 @@ around 10\,dB by the previous transformation pair~(Fig.\,\ref{fig:log-hp}cd) \end{figure} \FloatBarrier +\subsubsection{Field data} + +\begin{figure}[!ht] + \centering + \includegraphics[width=\textwidth]{figures/fig_invariance_field.pdf} + \caption{\textbf{Step-wise emergence of intensity invariant song + representation along the model pathway.} + } + \label{fig:pipeline_field} +\end{figure} +\FloatBarrier + +\subsection{Interspecific and intraspecific feature variability} + +In the final analysis of the current study, we investigated the variability of +songs in the feature representation between different species and within the +same species~(Fig.\,\ref{fig:feat_cross_species}). Naturally, a feature +representation that is both consistent across different songs of the same +species and sufficiently different between songs of different species is a +fundamental prerequisite for species-specific song recognition. The data used +in this analysis corresponds to the saturated $\muf$ of each $f_i(t)$ from the +previous analysis of the full model pathway~(Fig.\,\ref{fig:pipeline_full}c), +using different songs of \textit{O. rufipes} for the intraspecific comparisons +and single songs from a number of species for the interspecific comparisons +(also shown in Fig.\,\ref{fig:thresh-lp_species}a). Accordingly, each song is +represented by 40 values of $\muf$ based on the same set of $f_i(t)$. For each +comparison, $\muf$ from one song was plotted against $\muf$ from the other +song, so that each dot within a subplot corresponds to a single feature +$f_i(t)$. For the intraspecific +comparisons~(Fig.\,\ref{fig:feat_cross_species}, upper triangular), the pairs +of $\muf$ are distributed closely around the diagonal, with a minimum +correlation coefficient of $\rho=0.85$, a maximum of $\rho=0.99$, and a median +of $\rho=0.92$. A given $f_i(t)$ thus tends to have a similar $\muf$ across +different songs of the same species. In contrast, the pairs of $\muf$ for the +interspecific comparisons~(Fig.\,\ref{fig:feat_cross_species}, lower +triangular) are distributed in a variety of different ways, most in broader +clouds (e.g. \textit{C. biguttulus} vs. \textit{C. mollis}) but some more +narrowly around the diagonal (e.g. \textit{P. parallelus} vs. \textit{C. +dispar}). The correlation coefficients $\rho$ vary widely between different +interspecific comparisons, with a minimum of $\rho=-0.1$, a maximum of +$\rho=0.92$, and a median of $\rho=0.53$. A given $f_i(t)$ therefore tends to +have a less similar $\muf$ across different species than within the same +species, although certain exeptions exist~(Fig.\,\ref{fig:feat_cross_species}, +lower right). Accordingly, the feature representation that is generated by the +model pathway is, in principle, suitable for the distinction between different +species-specific songs. However, even the songs of the same species are subject +to considerable variability in various aspects and depending on a multitude of +external and internal factors, which cannot be fully captured based on a +limited number of songs. The results of the current analysis are hence to be +treated as a proof-of-concept that paves the way towards more comprehensive +investigations on the details of song representation in feature space, +including the effects of different parameters of the model pathway as well as +the inclusion of additional songs and species to reflect the complexity of +natural song variation. + \begin{figure}[!ht] \centering \includegraphics[width=\textwidth]{figures/fig_features_cross_species.pdf} @@ -1086,7 +1311,7 @@ around 10\,dB by the previous transformation pair~(Fig.\,\ref{fig:log-hp}cd) \textbf{Upper triangular}:~Intraspecific comparisons between different songs of a single species (\textit{O. rufipes}). - \textbf{Lower left}:~Distribution of correlation + \textbf{Lower right}:~Distribution of correlation coefficients $\rho$ for each interspecific and intraspecific comparison. Dots indicate single $\rho$ values. @@ -1095,16 +1320,6 @@ around 10\,dB by the previous transformation pair~(Fig.\,\ref{fig:log-hp}cd) \end{figure} \FloatBarrier -\begin{figure}[!ht] - \centering - \includegraphics[width=\textwidth]{figures/fig_invariance_field.pdf} - \caption{\textbf{Step-wise emergence of intensity invariant song - representation along the model pathway.} - } - \label{fig:pipeline_field} -\end{figure} -\FloatBarrier - \section{Conclusions \& outlook} \textbf{Song recognition pathway: Grasshopper vs. model:}\\ diff --git a/python/fig_features_cross_species.py b/python/fig_features_cross_species.py index 91b2728..e256fd5 100644 --- a/python/fig_features_cross_species.py +++ b/python/fig_features_cross_species.py @@ -2,7 +2,7 @@ import plotstyle_plt import numpy as np import matplotlib.pyplot as plt from itertools import product -from scipy.stats import ttest_ind +from scipy.stats import ttest_ind, mannwhitneyu from thunderhopper.modeltools import load_data from thunderhopper.filetools import search_files from thunderhopper.filtertools import find_kern_specs @@ -15,7 +15,7 @@ from IPython import embed # GENERAL SETTINGS: cross_species = [ 'Chorthippus_biguttulus', - # 'Chorthippus_mollis', + 'Chorthippus_mollis', 'Chrysochraon_dispar', # 'Euchorthippus_declivus', 'Gomphocerippus_rufus', @@ -410,11 +410,17 @@ for x, y in product(range(n_song), range(n_song)): # print('\nAxis position: ', (y, x)) # print(f'Song {song_labels[x]} (x) vs. Song {song_labels[y]} (y)') +print('\nMedian correlation coefficients:') +print(f'Intraspecies: {np.median(song_regs)}') +print(f'Interspecies: {np.median(spec_regs)}') + if test_regression: + song_regs, spec_regs = np.array(song_regs), np.array(spec_regs) + # Add test result subplot: test_ax = fig.add_subplot(test_ax_bounds) test_ax.set_xlim(-0.6, 1.6) - test_ax.set_ylim(0, 1) + test_ax.set_ylim(-0.15, 1) test_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc_test)) ylabel(test_ax, ylab_test, **ylab_test_kwargs) @@ -425,15 +431,22 @@ if test_regression: test_ax.plot(np.zeros(len(spec_regs)), spec_regs, **boxplot_dot_kwargs) test_ax.plot(np.ones(len(song_regs)), song_regs, **boxplot_dot_kwargs) + # CAREFUL - PSEUDO-REPLICATION: + # Perform t-test: test = ttest_ind(spec_regs, song_regs, equal_var=False) - t, p = test.pvalue, test.statistic + p, t = test.pvalue, test.statistic print(f'\nT-test result: t={t}, p={p}') + # Perform Wilcoxon rank-sum test: + test = mannwhitneyu(spec_regs, song_regs, alternative='two-sided') + p, u = test.pvalue, test.statistic + print(f'\nMWU rank test result: U={u}, p={p}') + if save_path is not None: fig.savefig(save_path) plt.show() - +embed() diff --git a/python/fig_invariance_thresh-lp_single.py b/python/fig_invariance_thresh-lp_single.py index 733f83a..5c4dee1 100644 --- a/python/fig_invariance_thresh-lp_single.py +++ b/python/fig_invariance_thresh-lp_single.py @@ -149,7 +149,7 @@ lw = dict( ) xlabels = dict( alpha='scale $\\alpha$', - sigma='$\\sigma_{\\text{adapt}}$', + sigma='$\\sigma_{\\text{adapt}}[\\text{dB}]$', ) ylabels = dict( inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$', diff --git a/python/save_inv_data_full.py b/python/save_inv_data_full.py index d19f4e9..d2fde87 100644 --- a/python/save_inv_data_full.py +++ b/python/save_inv_data_full.py @@ -13,12 +13,12 @@ from IPython import embed target_species = [ 'Chorthippus_biguttulus', 'Chorthippus_mollis', - # 'Chrysochraon_dispar', - # 'Euchorthippus_declivus', - # 'Gomphocerippus_rufus', - # 'Omocestus_rufipes', - # 'Pseudochorthippus_parallelus', -][1] + 'Chrysochraon_dispar', + 'Euchorthippus_declivus', + 'Gomphocerippus_rufus', + 'Omocestus_rufipes', + 'Pseudochorthippus_parallelus', +][0] example_file = { 'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms', 'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms', @@ -28,7 +28,7 @@ example_file = { 'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms', 'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms' }[target_species] -data_paths = search_files(target_species, incl='GBC', dir='../data/processed/') +data_paths = search_files(target_species, incl='DJN', dir='../data/processed/') noise_path = '../data/processed/white_noise_sd-1.npz' thresh_path = '../data/inv/full/thresholds.npz' stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat'] @@ -43,25 +43,22 @@ thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3]) # SUBSET SETTINGS: kernels = None -types = None -sigmas = None +types = None#np.array([1, -1, 2, -2, 3, -3, 4, -4]) +sigmas = None#np.array([0.001, 0.002, 0.004, 0.008, 0.016]) # PREPARATION: pure_noise = np.load(noise_path)['raw'] -thresh_data = dict(np.load(thresh_path)) -thresh_abs = thresh_rel[:, None] * thresh_data['sds'][None, :] +thresh_data = np.load(thresh_path)['sds'] +thresh_abs = thresh_rel[:, None] * thresh_data[None, :] # EXECUTION: for data_path, name in zip(data_paths, crop_paths(data_paths)): save_detailed = example_file in name print(f'Processing {name}') - if 'BM04' in name: - continue # Get song recording (prior to anything): data, config = load_data(data_path, files='raw') - song, rate = data['raw'], config['rate'] - print(song.shape, song.size) + song, rate = copy.deepcopy(data['raw']), config['rate'] # Reduce to kernel subset: if any(var is not None for var in [kernels, types, sigmas]): @@ -73,15 +70,19 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)): # Get song segment to be analyzed: time = np.arange(song.shape[0]) / rate - start, end = data['songs_0'].ravel() + start, end = copy.deepcopy(data['songs_0'].ravel()) segment = (time >= start) & (time <= end) + del data, time + gc.collect() # Normalize song component: song /= song[segment].std(axis=0) # Get normalized noise component: - noise = draw_noise_segment(pure_noise, song.shape[0]) + noise = copy.deepcopy(draw_noise_segment(pure_noise, song.shape[0])) noise /= noise[segment].std() + del pure_noise + gc.collect() # Prepare storage: shape_low = (scales.size,) @@ -128,6 +129,8 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)): snippets[f'snip_{stage}'][:, ..., scale_ind] = copy.deepcopy(signals[stage]) conv = copy.deepcopy(signals['conv']) + for stage in pre_stages: + del signals[stage] del scaled, signals gc.collect() @@ -161,7 +164,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)): archive.update(snippets) save_data(save_path + name, archive, config, overwrite=True) del archive - del measures, data, config, conv + del measures, config, conv if save_detailed: del snippets gc.collect() diff --git a/python/save_inv_data_short.py b/python/save_inv_data_short.py index 203400d..b2b08a6 100644 --- a/python/save_inv_data_short.py +++ b/python/save_inv_data_short.py @@ -10,14 +10,14 @@ from IPython import embed # GENERAL SETTINGS: target_species = [ - 'Chorthippus_biguttulus', + # 'Chorthippus_biguttulus', 'Chorthippus_mollis', 'Chrysochraon_dispar', 'Euchorthippus_declivus', 'Gomphocerippus_rufus', - 'Omocestus_rufipes', - 'Pseudochorthippus_parallelus', -][6] + # 'Omocestus_rufipes', + # 'Pseudochorthippus_parallelus', +][1] example_file = { 'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms', 'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',