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\subsection{Invariant processing in the grasshopper auditory system} \subsection{Invariant processing in the grasshopper auditory system}
\label{sec:general_inv} \label{sec:general_inv}
% Invariance in the general (systemic) sense: % Invariance in the general (systemic) sense (could be skipped if too much):
The notion of invariance is fundamental for sensory processing systems. The notion of invariance is fundamental for sensory processing systems.
Invariance, in the general sense, can be described as the property of a Invariance, in the general sense, can be described as the property of a
transformation to maintain variation across certain meaningful input parameters transformation to maintain variation across certain meaningful input parameters
@@ -1785,52 +1785,61 @@ that are robust to noise masking~(\bcite{einhaupl2011attractiveness}).
% Trading SNR for log-HP intensity invariance (+variability, +general principle): % Trading SNR for log-HP intensity invariance (+variability, +general principle):
The SNR of each song representation prior to $\adapt(t)$ increases The SNR of each song representation prior to $\adapt(t)$ increases
monotonically with $\sca$~(excluding $0<\sca\ll1$, noise regime). These monotonically with $\sca$~(excluding $0<\sca\ll1$, noise regime). These
representations maintain and improve the initial SNR of $\raw(t)$ and hence representations maintain the full extent of the initial SNR of $\raw(t)$ and
never achieve intensity invariance. In contrast, the SNR of the hence never achieve intensity invariance. In contrast, the SNR of $\adapt(t)$
intensity-invariant $\adapt(t)$ never exceeds its saturation level even for saturates for sufficiently high $\sca$. Accordingly, $\adapt(t)$ is
arbitrarily high $\sca$. The saturation level of $\adapt(t)$ varies across intensity-invariant but cannot have a higher SNR than the saturation level,
species and songs. This variability is likely rooted in the way in which which indicates a fundamental trade-off. The saturation level of $\adapt(t)$
logarithmic compression acts on the specific distribution of $\env(t)$, which varies across species and songs. This variability is likely rooted in the way
depends on the $\fc$ of the lowpass filter as well as the temporal structure in which logarithmic compression acts on the specific $\env(t)$, which depends
and frequency spectrum of the rectified $\filt(t)$. Overall, $\adapt(t)$ has on the $\fc$ of the lowpass filter as well as the temporal structure and
never been observed to exceed a SNR of around~10 across all songs. The low SNR frequency spectrum of the rectified $\filt(t)$. Across all songs, the
of $\adapt(t)$ partially results from the amplification of smaller values of saturation level of $\adapt(t)$ has never been observed to exceed a SNR of
$\env(t)$ by the logarithm, which raises the noise floor of $\adapt(t)$. Still, around~10. This is a substantial reduction in SNR, considering that the SNR of
the reduction in SNR is substantial --- considering that the SNR of preceeding preceeding representations had been orders of magnitude higher. Part of this
song representations has been orders of magnitude higher --- but is likely a reduction stems from the amplification of smaller values of $\env(t)$ by
necessary price to pay for the intensity invariance of $\adapt(t)$. After all, logarithmic compression, which raises the noise floor of $\adapt(t)$ relative
a transformation cannot compress a range of different input intensities into a to the song. Accordingly, the low SNR of $\adapt(t)$ appears to be a necessary
price to pay for its intensity invariance through logarithmic compression and
adaptation. But the trade-off between intensity invariance and SNR likely goes
beyond the particular mechanisms along the pathway. After all, a transformation
is not expected to compress a range of different input intensities into a
constant output intensity without sacrificing some of the corresponding input constant output intensity without sacrificing some of the corresponding input
SNR. Accordingly, the trade-off between intensity invariance and SNR is not SNR. This suggests that the trade-off is a more general principle that applies
expected to be specific to the particular mechanisms along the pathway but to any transformation that achieves or improves intensity invariance.
presumably applies to any transformation that achieves or improves intensity
invariance.
Thresholding and temporal averaging renders feature $f_i(t)$ The second mechanism of intensity invariance consists of thresholding and
intensity-invariant for sufficiently large $\sca$. The trade-off between temporal averaging of $c_i(t)$ into $f_i(t)$. Here, the trade-off between
intensity invariance and SNR is mediated by threshold value $\thr$. A lower intensity invariance and SNR is mediated by the threshold value $\thr$. A lower
$\thr$ ($\thr\to0$) improves intensity invariance by shifting the saturation $\thr$~($\thr\to0$) improves the intensity invariance of $f_i(t)$ by shifting
point towards lower $\sca$ but also decreases the SNR of $f_i(t)$. The the saturation point towards lower $\sca$. However, a lower $\thr$ also raises
saturation level of $f_i(t)$ is independent of $\thr$ as long as the intensity the noise floor of $f_i(t)$ by including more of the pure-noise $c_i(t)$, which
invariance by the previous mechanism is neglected. The SNR of $f_i(t)$ is decreases the SNR of $f_i(t)$. The distribution $\pci$ of the pure-noise
therefore determined solely by the pure-noise response of $f_i(t)$. The $c_i(t)$ is very close to a normal distribution with mean $\mu\approx0$ for all
distribution $\pci$ of the pure-noise kernel response $c_i(t)$ is largely a kernels $k_i(t)$. The value of the pure-noise $f_i(t)$ is hence 0.5 for
normal distribution with mean $\mu\approx0$ for all kernels $k_i(t)$. The value $\thr=0$ and decreases for higher $\thr$. If $\thr$ is set above the maximum of
of the pure-noise $f_i(t)$ is hence 0.5 for $\thr=0$ and decreases for higher $c_i(t)$, the pure-noise feature value is 0, which results in an "unlimited"
$\thr$. If $\thr$ is set above the maximum of $c_i(t)$, the pure-noise feature SNR of $f_i(t)$. In this case, any non-zero feature value that is sustained for
value is 0, which results in an "unlimited" SNR of $f_i(t)$. In this case, any a sufficient duration could serve as indicator for the presence of $\soc(t)$ in
non-zero feature value that is sustained for a sufficient duration could serve $\raw(t)$, although at the cost of a higher saturation point. Of course, this
as indicator for the presence of $\soc(t)$, although at the cost of a higher would require a fine evolutionary tuning of $\thr$ to the properties of the
saturation point. The maximum of the pure-noise $c_i(t)$ is assumed to be very natural noise in a certain habitat to avoid false positives.
The saturation level of $f_i(t)$ is independent of $\thr$ as long as the
intensity invariance by the previous mechanism is neglected.
If $f_i(t)$ can achieve an arbitrarily high SNR, it can counteract the
comparably low SNR of $\adapt(t)$
The maximum of the pure-noise $c_i(t)$ is assumed to be very
small due to the various SNR improvements along the pathway, so that the small due to the various SNR improvements along the pathway, so that the
required increase in $\thr$ and hence the saturation point of $f_i(t)$ is not required increase in $\thr$ and hence the saturation point of $f_i(t)$ is not
expected to be substantial. However, exploiting the capacity of $f_i(t)$ for expected to be substantial.
arbitrarily high SNR would certainly require a fine evolutionary tuning of
$\thr$ to the properties of both the species-specific song and the natural
noise in a certain habitat.
\newpage
\subsection{Intensity invariance versus intensity invariance} % \newpage
% \subsection{Intensity invariance versus intensity invariance}
Two consecutive mechanisms of intensity invariance do not necessarily add up to Two consecutive mechanisms of intensity invariance do not necessarily add up to
a stronger overall intensity invariance. If the first mechanism results in a a stronger overall intensity invariance. If the first mechanism results in a