Bullet-pointing a first draft (WIP).

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-\begin{document}
+\usepackage{parskip}
 
+\title{Physiologically inspired model of the grasshopper auditory system}
+\author{Jona Hartling, Jan Benda}
+\date{}
+
+\begin{document}
+\maketitle{}
+
+\section{The sensory world of a grasshopper}
+
+Strong dependence on acoustic signals for ranged communication\\
+- Diverse species-specific sound repertoires and production mechanisms\\
+- Different contexts/ranges: Stridulatory, mandibular, wings, walking sounds\\
+- Mate attraction/evaluation, rival deterrence, loss-of-signal predator alarm\\
+$\rightarrow$ Elaborate acoustic behaviors co-depend on reliable auditory perception
+
+Songs = Amplitude-modulated (AM) broad-band acoustic signals\\
+- Generated by stridulatory movement of hindlegs against forewings\\
+- Shorter time scales: Characteristic temporal waveform pattern\\
+- Longer time scales: High degree of periodicity (pattern repetition)\\
+- Sound propagation: Signal intensity varies strongly with distance to sender\\
+- Ectothermy: Temporal structure warps with temperature\\
+$\rightarrow$ Sensory constraints imposed by properties of the acoustic signal itself
+
+Multi-species, multi-individual communally inhabited environments\\
+- Temporal overlap: Simultaneous singing across individuals/species common\\
+- Frequency overlap: No/hardly any niche speciation into frequency bands\\
+- "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{Pre-split pathway: Population pre-processing}
+Filtering of behaviorally relevant frequencies by tympanal membrane\\
+- Bandpass 5-30 kHz
+
+Extraction of signal envelope (AM encoding) by receptor population\\
+- Full-wave rectification + lowpass 500 Hz
+
+Logarithmically compressed intensity tuning curve of receptors\\
+- Decibel transformation
+
+Spike-frequency adaptation in receptor and interneuron populations\\
+- Highpass 10 Hz
+
+\section{Post-split pathway: Feature extraction}
+
+Template matching by individual ascending neurons\\
+- Separate convolution with each of a set of Gabor kernels\\
+- Pathway splitting: Single population response into several separate branches
+- Expansion into a higher-dimensional sound representation
+
+Thresholding nonlinearity in ascending neurons (or further downstream)\\
+- Step-function (or sigmoid) threshold\\
+- Binarization of response values into "relevant" vs. "irrelevant"
+
+Temporal averaging by neurons of the central brain\\
+- Lowpass 1 Hz\\
+- Finalized set of slowly changing kernel-specific features\\
+- Different (species-specific) songs are characterized by a distinct combination of feature values
+
+\section{Pre-split intensity invariance:\\Logarithm-highpass mechanism}
+
+\section{Post-split intensity invariance:\\Threshold-lowpass mechanism}
+
+\section{Conclusion and outlook}
 
 \end{document}