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@@ -179,39 +179,21 @@ functional model framework thus requires a considerable amount of
simplification. In this work, we demonstrate that even a small number of basic
physiologically inspired signal transformations --- specifically, pairs of
nonlinear and linear operations --- is sufficient to achieve a meaningful
degree of intensity invariance. Due to the critical role of intensity-invariant
representations for reliable song recognition, these transformations are at the
core of the proposed model framework.
degree of intensity invariance.
Invariance to non-informative signal variations is a crucial property of song
representations that are suitable for the purpose of song recognition. However,
it is likely not sufficient on its own. The auditory system also needs to
extract sufficiently informative song features in order to reliably
discriminate between conspecific and heterospecific song patterns. Other
authors have proposed a comprehensive physiologically inspired framework
to describe the process of feature extraction based on linear-nonlinear
modelling~(\cite{clemens2013computational}, \cite{clemens2013feature},
\cite{ronacher2015computational}), which represents an important precursor and
cornerstone for the model we present here.
Multi-species, multi-individual communally inhabited environments\\
- Temporal overlap: Simultaneous singing across individuals/species common\\
- Frequency overlap: Little speciation into frequency bands (likely unused)\\
- "Biotic noise": Hetero-/conspecifics ("Another one's songs are my noise")\\
- "Abiotic noise": Wind, water, vegetation, anthropogenic\\
- Effects of habitat structure on sound propagation (landscape - soundscape)\\
$\rightarrow$ Sensory constraints imposed by the (acoustic) environment
Cluster of auditory challenges (interlocking constraints $\rightarrow$ tight coupling):\\
From continuous acoustic input, generate neuronal representations that...\\
1)...allow for the separation of relevant (song) events from ambient noise floor\\
2)...compensate for behaviorally non-informative song variability (invariances)\\
3)...carry sufficient information to characterize different song patterns,
recognize the ones produced by conspecifics, and make appropriate behavioral
decisions based on context (sender identity, song type, mate/rival quality)
How can the auditory system of grasshoppers meet these challenges?\\
- What are the minimum functional processing steps required?\\
- Which known neuronal mechanisms can implement these steps?\\
- Which and how many stages along the auditory pathway contribute?\\
$\rightarrow$ What are the limitations of the system as a whole?
How can a human observer conceive a grasshopper's auditory percepts?\\
- How to investigate the workings of the auditory pathway as a whole?\\
- How to systematically test effects and interactions of processing parameters?\\
- How to integrate the available knowledge on anatomy, physiology, ethology?\\
$\rightarrow$ Abstract, simplify, formalize $\rightarrow$ Functional model framework
\textbf{Precursor work for model construction (special thanks to authors):}
@@ -227,6 +209,35 @@ $\rightarrow$ Now actual, variable songs (as naturalistic as possible)\\
2) Fitted filters to behavioral data\\
$\rightarrow$ More general, simpler, unfitted formalized Gabor filter bank
% SCRAPPED UNTIL FURTHER NOTICE:
% Multi-species, multi-individual communally inhabited environments\\
% - Temporal overlap: Simultaneous singing across individuals/species common\\
% - Frequency overlap: Little speciation into frequency bands (likely unused)\\
% - "Biotic noise": Hetero-/conspecifics ("Another one's songs are my noise")\\
% - "Abiotic noise": Wind, water, vegetation, anthropogenic\\
% - Effects of habitat structure on sound propagation (landscape - soundscape)\\
% $\rightarrow$ Sensory constraints imposed by the (acoustic) environment
% Cluster of auditory challenges (interlocking constraints $\rightarrow$ tight coupling):\\
% From continuous acoustic input, generate neuronal representations that...\\
% 1)...allow for the separation of relevant (song) events from ambient noise floor\\
% 2)...compensate for behaviorally non-informative song variability (invariances)\\
% 3)...carry sufficient information to characterize different song patterns,
% recognize the ones produced by conspecifics, and make appropriate behavioral
% decisions based on context (sender identity, song type, mate/rival quality)
% How can the auditory system of grasshoppers meet these challenges?\\
% - What are the minimum functional processing steps required?\\
% - Which known neuronal mechanisms can implement these steps?\\
% - Which and how many stages along the auditory pathway contribute?\\
% $\rightarrow$ What are the limitations of the system as a whole?
% How can a human observer conceive a grasshopper's auditory percepts?\\
% - How to investigate the workings of the auditory pathway as a whole?\\
% - How to systematically test effects and interactions of processing parameters?\\
% - 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 auditory pathway}
% Either pick up in intro and/or discussion, or move entirely: