Download Computational themes of peripheral processing

J Comp Physiol A (2015) 201:39–50
DOI 10.1007/s00359-014-0956-5
REVIEW
Computational themes of peripheral processing in the auditory
pathway of insects
K. Jannis Hildebrandt · Jan Benda ·
R. Matthias Hennig Received: 8 August 2014 / Revised: 10 October 2014 / Accepted: 11 October 2014 / Published online: 31 October 2014
© Springer-Verlag Berlin Heidelberg 2014
Abstract Hearing in insects serves to gain information
in the context of mate finding, predator avoidance or host
localization. For these goals, the auditory pathways of
insects represent the computational substrate for object recognition and localization. Before these higher level computations can be executed in more central parts of the nervous
system, the signals need to be preprocessed in the auditory
periphery. Here, we review peripheral preprocessing along
four computational themes rather than discussing specific
physiological mechanisms: (1) control of sensitivity by
adaptation, (2) recoding of amplitude modulations of an
acoustic signal into a labeled-line code (3) frequency processing and (4) conditioning for binaural processing. Along
these lines, we review evidence for canonical computations
carried out in the peripheral auditory pathway and show
that despite the vast diversity of insect hearing, signal processing is governed by common computational motifs and
principles.
Keywords Spike frequency adaptation · Neural coding ·
Sensory processing
K. J. Hildebrandt (*) Cluster of Excellence “Hearing4all”, Department
for Neuroscience, University Oldenburg, 26111 Oldenburg,
Germany
e-mail: jannis.hildebrandt@uni‑oldenburg.de
J. Benda Institute for Neurobiology, Eberhard Karls Universität Tübingen,
72076 Tübingen, Germany
R. M. Hennig Department of Biology, Humboldt-Universität zu Berlin,
10115 Berlin, Germany
Introduction
Many insects use information in the context of acoustic communication to find a mating partner or to avoid a
predator; some insects localize their hosts or deceive their
prey (Marshall and Hill 2009). For these overall goals,
sound has to be detected by auditory organs and processed
by neural circuits. The relevant aspects of sound to consider apply to auditory systems in general: the frequency
content, the pattern envelope, and binaural cues for localization. Early processing of auditory information resides
between the biophysical mechanisms of sound transduction (see contribution Montealegre-Z and Robert, Mhatre),
encoding, and object recognition (contribution Kostarakos
and Hedwig). The transition from the encoding of physical cues to perceptual categories may be particularly fast
in insects (Hildebrandt 2014), offering the opportunity to
study peripheral preprocessing in a confined part of the
pathway.
Despite the large variety of both mechanisms and relevant signals across different taxa of hearing insects, common computational themes for different aspects of sound
can be described. Our review is organized into four themes
that highlight the function of peripheral processing of these
different aspects of sound (Fig. 1): (1) Control of sensitivity by means of adaptation has an important role for the
initial representation of amplitude modulations (AM, envelope). (2) The recoding of the representation of AM into
sparse and de-correlated representations is an important step
towards central pattern recognition. (3) Peripheral shaping of frequency tuning is a crucial process of filtering relevant information. (4) Due to the small body size of insects,
peripheral mechanisms to condition signals for binaural processing are important for sound localization. From a computational point of view most progress of the last decades has
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J Comp Physiol A (2015) 201:39–50
Fig. 1 Schematic overview of
computational themes in the
peripheral processing of insect
audition as discussed in the
review. Icons on the left and
computational themes each
correspond to one part in the
main text
come from two physiologically well-described taxa, grasshoppers and crickets that we focus on in this review.
Neuronal adaptation: controlling sensitivity of the
auditory periphery
As in all sensory systems, the range of intensities and thus
amplitudes the auditory pathway of insects can code for
is limited. Due to the low frequency resolution of most
insects, a particularly large part of the usable information
about auditory signals resides in amplitude modulations
(contribution Ronacher et al. 2014; contribution Kostarakos
and Hedwig). Thus, an accurate encoding of the amplitude
of the stimulus in the periphery is essential (Fig. 1). Neuronal adaptation provides the means to adjust the mapping
of amplitudes to neuronal responses depending on the current sensory environment. In this part, we will first present
a short overview on how these adjustments can be experimentally approached by measuring input-response curves,
discuss how adaption acts on the input-response curves,
and then present three different computational roles of
adaptation in the auditory pathway of insects: improvement
of pattern coding, generation of invariance and signal–
background separation.
Probing adaptation: input–response curves
The mapping of stimulus amplitudes to neural responses
is described by their input-response curves. This mapping
is usually limited by a threshold at the lower intensity end
and by saturation at the highest possible response at the
higher end of the intensity axis. Only stimulus values that
fall within the dynamic range between these borders can be
reliably encoded. When the sensory pathway adapts, position and shape of the response curve and consequently the
dynamic range changes (Fig. 2). To understand the computational function of adaptation, an appropriate quantitative description is necessary. This includes the separation
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of onset responses that characterize the mapping of fast
amplitude modulations of the stimulus to neural responses
from steady-state responses that describe encoding of slow
amplitude modulations (Fig. 2b). Fast and slow stimulus
components are separated by the adaptation time constant.
Shape and position of the onset response curve depend on
the state of adaptation (Fig. 2d, e). Steady state curves can
be probed by prolonged presentation of constant stimuli
at different levels. Onset curves are measured by applying short, transient level changes at different background
intensities (Fig. 2a, b; Benda and Herz 2003). The exact
way in which adaptation changes the onset response curve
is important because it determines its functional role in the
pathway.
Subtractive and divisive adaptation
The way adaptation changes the onset response curve
of the sensory periphery determines which and how
much information about a given stimulus is available at
more central stages. In most cases, these changes can be
described as either subtractive or divisive (Fig. 2c; Hildebrandt et al. 2011). While subtractive changes adjust the
threshold of a curve and thus its sensitivity, divisive adaptation alters the slope and therefore the range of levels that
can be encoded at the expense of resolution. Which of
these two is at work should depend on the goal of encoding. Many insect pathways are specialized for the processing of highly stereotyped conspecific signals (see contribution Stumpner et al.), which vary mostly in mean level
and may only require threshold shifts. Accordingly, in the
ascending neuron AN2 of crickets, response to stimuli
with the low carrier frequency of conspecific songs displayed only subtractive shifts (Fig. 2d; Hildebrandt et al.
2011). At higher frequencies, where encoding may serve
more variable purposes ranging from high frequency components of conspecific signals to diverse predator sounds,
adaptation acted both in a subtractive and divisive manner
(Fig. 2e).
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a
c
b
d
e
Fig. 2 Adapted input-response curves. a Experimental protocol to
obtain adapted response curves: test steps are presented as upward
or downward transients of an adapting background and the initial
response of the neuron to the change under study is quantified (green
dots). Steady-state responses (red) are measured after the response of
the neuron has decayed. b Example of a steady-state response curve
(red line) and adapted onset response curve (black). Green and red
markers correspond to responses at the time points marked in (a). c
Changes of onset responses curves are usually either subtractive (left
panel), shifting the curve along the intensity axis, or divisive (right
panel), changing the slope of the response curve, or a combination
of both. d, e Different operations imposed by adaptation in a cricket
ascending interneuron (AN2) when tested at either conspecific carrier
frequencies (d) or predator-like high frequencies (e). Shown are onset
response curves measured at different background intensities (vertical
lines, corresponding symbols indicate the background intensity for
each response curve). At low frequencies, normalization to the background revealed purely subtractive adaptation (d), at high frequencies, adaptation also has a divisive component (e). Modified from
Hildebrandt et al. (2011)
Adaptation and pattern copying fidelity
played back at different distances or at different levels. An
important computational role of adaptation in the periphery
may thus be the creation of intensity-invariant representation of relevant signals as input to central pattern processing (contribution Kostarakos and Hedwig). An example for
such invariant coding can be found at the level a primary
auditory interneuron (AN1) of crickets (Benda and Hennig
2008; Fig. 3a). In the auditory pathway of grasshoppers,
adaptation balances the rising intensity of consecutive syllables already at the receptor level (Machens et al. 2001).
The most obvious computational role for sensory adaptation in the periphery is to flexibly enhance sensory representation. For amplitude-modulated sound, this means that
the onset response curve should be adjusted to the distribution of amplitudes in the relevant signal (Laughlin 1981).
In a behavioral study, grasshoppers needed fewer syllables of a conspecific song to respond when the song was
preceded by an adapting stimulus that was otherwise not
attractive (Ronacher and Hennig 2004). The initial part of
the songs may thus be needed to adjust peripheral coding
to the amplitude distribution before pattern information can
be used centrally. Results from electrophysiological studies
on the receptors support this role of adaptation: the receptors were found to quickly adapt to the stimulus level (Gollisch and Herz 2004). Other studies showed that receptors
reliably encode of conspecific songs in varying conditions
(Machens et al. 2001, 2003).
Invariance
If adaptation results in optimal representation of a signal,
this representation should not be different if the stimulus is
Foreground–background separation
In some cases, the loss of information as a result of adaptation may not be detrimental but desirable. If adaptation acts
subtractively on the response curves (Fig. 2c), it can result
in a threshold shift that separates relevant signals from a
deteriorating background (Fig. 3b). In the omega neuron of
crickets (ON1), accumulating Ca2+ changes the excitability
of the ON1 in such a way that only the louder of two conspecific songs is represented (Pollack 1988; Sobel and Tank
1994; Baden and Hedwig 2006). However, this mechanism
may not generalize for different foreground/background
combinations. In a more elaborate approach, Wimmer et al.
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J Comp Physiol A (2015) 201:39–50
and Pollack 2006), where movements of the abdomen away
from the sound source coincide with bursts.
a
Recoding the representation of acoustic signals
b
c
Fig. 3 Adaptation enables invariant coding of amplitude-modulated
sounds. a Responses of a cricket AN1 interneuron (lower panel) to
amplitude-modulated sound played back at three different intensities (upper panel) are shown. After the initial adaptation phase the
responses to the two louder stimuli are similar, i.e., the response
of the interneuron is intensity invariant. b, c Possible strategies for
encoding sounds of different intensity distributions. b For signal–
background segregation the neuron’s response function (blue solid
line, right axis) should only cover the loudest component of the
stimulus (right hump of a bimodal stimulus distribution, blue-shaded
areas and left axis). When a third stimulus component is added (red)
the response curve should shift to that additional stimulus component and cut out the two softer ones. c For optimal coding the whole
stimulus the response curve must become wider when the third stimulus component is added. Panel a modified from Benda and Hennig,
(2008), b and c from Wimmer et al. (2008)
(2008) tested adaptation in the AN2 of crickets with bi- and
trimodal distributions of stimulus amplitudes to test the
two rivaling hypothesis of background separation (Fig. 3b),
where adaptation shifts the response curve to the loudest stimulus component, versus optimal coding (Fig. 3c),
where the whole stimulus including the background components should be covered by the response curve. Instead
of removing the “background signal” from the representation, adaptation shifted the response simply by the mean
of the summed distribution. Since for most insects only a
very specific set of stimuli is relevant, such simple rules for
adaptation may be sufficient for the tasks. An alternative
way to increase the signal-to-noise ratio of behavioral relevant signals is to encode them in bursts of spikes. This has
been demonstrated for the AN2 neuron of crickets (Marsat
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Adaptation processes play an important role in achieving
an intensity-invariant representation of the amplitude modulation of the perceived signal and to increase the signalto-noise ratio. Next, the representation of the amplitude
modulation of a signal by the auditory receptor neurons
(Machens et al. 2001) needs to be processed in a way that
higher level neurons are able to compute the correct decision in a robust way (Creutzig et al. 2009; Fig. 1). In the
following, we focus on the well-investigated transformation
and representation of calling song signals in the early auditory pathway of grasshopper.
The auditory periphery of grasshopper forms a threelayered feed-forward network (Vogel and Ronacher 2007).
In this network, 60–80 receptor neurons converge onto 10–
15 local neurons in the meta-thoracic ganglion. The local
neurons in turn project onto 15–20 ascending neurons that
convey information to the supraesophageal ganglion, where
eventually the decision about song identity is computed
(Fig. 4a). As the spike responses from three example cells
of each of the three layers already demonstrate, the mean
firing rate decreases and the response variability increases
in higher layers of the network (Fig. 4c; Vogel et al. 2005;
Wohlgemuth and Ronacher 2007; Clemens et al. 2011). In
addition, while single receptor neurons and local neurons
faithfully represent the amplitude modulation of the song
(Machens et al. 2001), individual ascending neurons represent only certain features of the song (Fig. 4c). Ascending neurons form a heterogeneous population with individual and different coding properties (Fig. 4b; Clemens
et al. 2011; Wohlgemuth et al. 2011). The representation
of acoustic signals is thus transformed from a population
code of similarly responding receptor neurons that precisely copy the stimulus envelope, to a labeled-line code at
the level of the ascending neurons, where each individual
cell codes for a specific feature of the input signal (Clemens et al. 2011).
Feed-forward inhibition seems to play an important role
in forming this labeled-line code. This has been explicitly
demonstrated for the AN12 that encodes pause duration by
the number of spikes in a burst fired at the onset of a syllable (Fig. 5a, b; Stumpner and Ronacher 1991; Creutzig
et al. 2009, 2010), as well as for the AN4 whose response
is inhibited if gaps within a syllable become to large
(Stumpner and Ronacher 1994). However, for many other
ascending neurons it is not clear which aspect of the song
they encode. Spike-triggered average (Fig. 4b) and spike-triggered covariance analysis computed for ascending neurons
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a
b
c
Fig. 4 Recoding the representation of sound signals in the auditory
pathway of grasshopper. a Grasshopper songs are processed by a
three-layered feed-forward network consisting of a receptor neuron,
local neuron, and ascending neuron level. b Spike-triggered average
filters of receptor neurons (blue, 10 cells), local neurons (red, 21 cells
of five types), and ascending neurons (green, 25 cells of seven types).
Black line is the average over all cells and types. Note the larger
diversity for the filters of the ascending neurons. c Measures quantifying the transition from a summed-population code to a labeled-line
code: The average firing rate in response to 8 different songs of Chorthippus biguttulus is decreased. The response similarity between different cells in the same layer of the network (correlation coefficient
between pairs of spike trains from different cells) drops to zero for
the ascending neurons. This is in accordance with the filter similarity
(correlation coefficient between the spike-triggered average filters)—
the filters of the ascending neurons are the least similar (see panel b).
All plots modified from Clemens et al. (2011)
suggests that different delays of inhibitory input generates
the different response profiles of ascending neurons (Clemens et al. 2012). In this way, each ascending neuron codes
for the presence of a different property, called “feature”, of
the song. These features do not need to correspond to an
obvious aspect of a song, as in the case of the AN4 (gap
encoder) and AN12 (pause encoder), but each of these features emphasize a different aspect.
This labeled-line representation of the songs, where each
individual ascending neuron codes for a different feature,
provides several advantages: First, this code optimally uses
limited resources by a de-correlated and sparse representation of the signals (Barlow 2001; Clemens et al. 2011).
Second, behavioral experiments suggest an integration time
scale in the range of 500 ms to one second (von Helversen
and von Helversen 1994; Ronacher et al. 2000). By integrating over the responses of each ascending neuron the
repetitive structure of grasshopper songs can be exploited
to improve the signal-to-noise ratio for the represented feature. This would not be possible based on the responses of
the receptor neurons, because averaging over such a direct
representation of the song’s amplitude modulation would
simply result in the mean intensity of the song, independent of its structure. Third, integrating over features is the
basis for a time-warp invariant recognition of songs. This
problem arises for example when the syllable–pause ratio
of a song needs to be evaluated independent of the temperature at which the song was generated (Fig. 5a, b; Creutzig
et al. 2009). However, this comes at the cost of lost information about the temporal order of individual features (von
Helversen and von Helversen 1998; Creutzig et al. 2009;
Clemens and Hennig 2013; contribution Ronacher et al.
2014).
An important implication of such a labeled-line code
was pointed out by Clemens and Ronacher (2013). Only for
one type of ascending neuron a precise match of neuronal
activity with behavioral selectivity is known (Ronacher and
Stumpner 1988). However, although most other ascending
neurons do not show an obvious relation to the behavioral
response, their integrated and linearly combined response
potentially explains the behavioral tuning remarkably well
(contribution Ronacher et al. 2014). Thus, assessing the
role of each individual ascending neuron in explaining the
behavioral response is not sufficient. Only by analyzing
the labeled-line code of the whole population of ascending neurons their role in shaping the behavioral tuning will
become obvious.
The recoding of songs into a set of various features in
the auditory pathway of grasshoppers makes this system very flexible in adapting to different songs on an
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Neuronal variability and noise
Fig. 5 Time-warp invariance by integrating the response of ascending neurons. a Model prediction for the AN12 that fires a burst of
spikes (strokes) at the onset of a syllable (gray blocks). The number
of spikes in the burst correlates with the length of the pause separating the syllables. Since time-warping a song changes both the pause
duration and the syllable period by the same factor, integrating over a
fixed time window always results in the same spike count (marked at
the right) for a fixed syllable/pause ratio. b The model prediction in
(a) is confirmed by experimental data: AN12 spike trains in response
to time-warped C. biguttulus songs. From Creutzig et al. (2009)
evolutionary time scale. Only a few weights for reading
out the ascending neurons need to be modified to adapt a
new species to a different song—this might explain the sensory aspect of why closely related species often differ dramatically in their songs. The auditory periphery is indeed
highly conserved between grasshopper species for about
50 millions years (Neuhofer et al. 2008). Most likely, songs
have evolved to exploit this representation (Clemens et al.
2010) and not vice versa (Machens et al. 2005).
In crickets, both receptor neurons and ascending neurons
directly represent the sound envelope (Benda and Hennig
2008). The firing pattern of auditory brain interneurons still
closely follows the temporal pattern of a stimulus (Kostarakos and Hedwig 2012, this issue), although the average
firing rate of these neurons is reduced (Schildberger 1984;
Zorovic and Hedwig 2011; Kostarakos and Hedwig 2012).
The tuning of some but not all of the brain interneurons
directly matches behavioral responses with respect to pulse
rate (Kostarakos and Hedwig 2012; but see Clemens and
Hennig 2013). Indeed, behavioral experiments suggest
that crickets do not evaluate the detailed structure of sound
pulses (Schneider and Hennig 2012). Based on the available data it seems that auditory processing in crickets does
not require an abstract representation of multiple features
as in grasshoppers.
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As mentioned above, the responses of ascending neurons
are quite variable, i.e., in response to the very same stimulus the number of spikes as well as their timing might vary
considerably (Vogel et al. 2005; Clemens et al. 2011; Neuhofer et al. 2011; Neuhofer and Ronacher 2012; Fig. 4c).
The variability intrinsic to the auditory pathway can be
of similar magnitude as extrinsic variability due to signal
degradation (Neuhofer et al. 2011). However, the abovementioned readout of features coded by ascending neurons
over long integration times is robust against the variability in spike timing (Creutzig et al. 2009, 2010; Clemens
and Ronacher 2013). Also, the variability in spike count
in response to a single syllable is averaged out by integrating over several syllables (Ronacher et al. 2000). If channel noise both in spike generating and synaptic processes
does not need to be reduced to a minimum, then no strong
selection for precise spike timing in the ascending neurons
is expected.
However, cell-intrinsic noise might play a role in shaping the response properties of auditory neurons. The main
noise sources driving the variability of spike responses in
receptor neurons is channel noise generated most likely
from the receptor current and from an adaptation current
(Fisch et al. 2012). This noise is small enough to allow for
very precise responses to fast and strong increases in sound
amplitude, yet is large enough to result in imprecise spiking to slowly changing stimuli of small amplitude, reducing
the mutual information (Rokem et al. 2006; Machens et al.
2001). Note, however, that not all receptor neurons encode
the stimulus equally well since they are tuned to different
carrier frequencies and have different sensitivities. Therefore, only a fraction of receptor neurons contributes to an
improved representation of the stimulus in local neurons.
An additional mechanism for emphasizing salient signals is
stimulus-driven bursts. These bursts encode stronger stimulus deflections reliably. Bursts of the AN2 of crickets have
been shown to correlate with steering movements of the
abdomen, whereas isolated spikes do not (Marsat and Pollack 2006, 2010; Eyherabide et al. 2008; Krahe and Gabbiani 2004).
In the next step, the reliable code of the local neurons is transformed to highly variable responses of the
labeled-line code formed by the ascending neurons, by
yet unknown mechanisms and noise sources. Intracellular
recordings with current injections suggest that the process of spike generation in ascending (and local) neurons
is much more precise than the response to acoustic stimulation (Hildebrandt et al. 2009). This leaves synaptic and
network effects, in particular the interplay of feed-forward
excitation and inhibition, as the main source of the large
variability observed in the ascending neurons. The reduced
J Comp Physiol A (2015) 201:39–50
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Noise filtering
Fig. 6 Processing of frequency information in the early auditory
system of insects. Scheme of typical early frequency processing as
observed in crickets and grasshoppers: the sensitivity of the tonotopically organized hearing organ already filters out some of the irrelevant background noise, for example abiotic sound sources like wind.
In the next step, spectral information is combined into very few, typically one or two semantic channels that represent conspecific signal
or predators. The integration of spectral cues usually includes both
lateral inhibition and summation across a specific spectral band
firing rates observed in ascending neurons indicate that
they might operate in the fluctuation-driven regime, i.e.,
closer at the spiking threshold. In that way, the non-linearity of the spike threshold might be better exploited, making their responses more specific, and at the same time
more susceptible to channel noise and thus more variable.
However, because of the integration of these responses
(Fig. 5), this variability will be easily averaged out to
result in precise decisions (Creutzig et al. 2010; Clemens
and Hennig 2013).
Shaping of frequency tuning
Most insects use the spectral content of auditory stimuli to
filter and process relevant information (Fig. 1). The foundation for the sensitivity to a particular bandwidth of frequencies is laid by the mechanical construction of the tympanum and the auditory organ (Fig. 6). As in vertebrates,
traveling waves and active processes in hearing are now
known for insects (see contribution Montealegre-Z and
Robert, and Mhatre in this issue). Despite this similarity
to vertebrate hearing organs, the most prominent computational roles for neural frequency processing in insects are
(1) filtering of noise and (2) formation of frequency channels by pooling information across extended spectral bands
aided by lateral inhibition. Apart from these most important
roles, a (3) finer spectral resolution at the level of receptors
may serve very specific computational functions more centrally in some species of insects.
To process a relevant signal, it needs to be separated from
other sound sources in the background. If the spectra of
background and signal are sufficiently different, frequency
filtering provides an efficient means for noise reduction.
For abiotic sound sources the low frequency band below
1 kHz dominates (Klump and Shelter 1984; Bradbury and
Vehrencamp 1998). The tympanic ears of most insects filter this usually irrelevant frequency range as their sensitivities are located above 2 kHz (Fig. 6; Gerhardt and Huber
2002, see, however, Lampe et al. (2012) for the influence
of anthropogenic noise). Sensitivity given by thickness
and compliance of the tympanum is a mechanical filter
that aides noise reduction (see also mechanical device for
direction below). However, many biotic sound sources
have similar or identical spectral content and are thus likely
the hardest challenge for the overall goals of processing
(Römer et al. 1989). Neural filters that act as finely tuned
spectral band passes can provide a solution to this problem
(Schmidt et al. 2011).
Importantly, the definition of noise often depends on
context and the specific task to be solved by auditory processing. While conspecific mating sounds are usually the
most relevant signal to be passed on, the presence of a
predator may render these sounds irrelevant in that context
and thus a source of noise that obstructs proper processing
of the predator sound. In the limited auditory pathways of
most insects, separating spectral information into semantic
channels may provide a solution to this problem (Figs. 1,
6).
Formation of frequency channels
Frequency sensitivity in the auditory organs of insects
covers a wide spectral range from 2 to 100 kHz. In many
species, individual sensory cells are tuned to different frequencies, however, almost all species form only one or two
frequency channels in their auditory pathways (Stumpner
and von Helversen 2001). For numerous taxa spectral sensitivity covers only a single frequency band dedicated to
the ultrasonic range as in lacewings, flies, moths, tiger beetles and mantids (Hoy 1989; Stumpner and von Helversen
2001). Several taxa exhibit sensory cells tuned to different frequencies that could in principle be used for fine frequency resolution based on the map-like representation of
axonal projections in the auditory neuropile (Hildebrandt
2014). However, spectral information is usually pooled into
only two frequency channels very early in the auditory processing chain, most prominently in crickets and grasshoppers (Stumpner and von Helversen 2001; Fig. 6). From a
functional point of view the formation of frequency channels is known to serve a variety of tasks: (1) to distinguish
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conspecific signals from predators (e.g., categorical perception in crickets, Wyttenbach et al. 1996), (2) species discrimination in crickets and tettigoniids (Hill 1974; Hennig
and Weber 1997; Schul et al. 1998), (3) sex discrimination
in grasshoppers and tettigoniids (von Helversen and von
Helversen 1997; Dobler et al. 1994), (4) stream segregation in katydids as evident in a single interneuron (Schul
and Sheridan 2006) and (5) range fractionation for intensity
discrimination (Römer et al. 1998).
Even though a higher spectral resolution can often be
observed in the hearing organs, most insects process sound
in one or two channels with few exceptions (Phaneropteridae, Ensifera, retain the differential tuning to carrier frequencies in the auditory pathway, Stumpner 2002). Thus,
frequency information is rapidly merged after the receptor level. Both summation and lateral inhibition can be
observed in this process (Fig. 6). Frequency-specific inhibitions are known for many interneurons in several auditory pathways, especially tettigoniids (Fig. 6; Stumpner
2002). In some cases, lateral inhibition sharpens the tuning of interneurons and serves to discriminate the acoustic
signals of sexes (Stumpner 2002). Intriguingly, the tuning
observed for interneurons is generally not higher than that
known for sensory neurons with few exceptions from katydids (Stumpner 1997; Hennig et al. 2004). However, tuning
in almost all studies on central frequency was determined
with only a single frequency band present at a time. In a
more realistic setting, when multiple spectrally distinct signals are presented in a mix, lateral inhibition may enhance
separation of different frequency channels which are otherwise broadly tuned, possibly allowing for flexible, contextdependent frequency tuning.
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processing levels as frequency information is pooled by
local and ascending interneurons. As relevant song signals
of katydids are commonly broadband and higher frequencies are attenuated more over distance, the seemingly detrimental pooling of frequencies allows to code for distance
of a signaler by the activation level of interneurons (Römer
1987). Furthermore, summation over numerous receptors
with different frequency sensitivities may also increase
the dynamic range for the processing of sound intensity
(Römer et al. 1998). In essence, such a range fractionation
is in part an alternative solution to maintain sensitivity at
different intensity levels and evident in different thresholds
of sensory cells in crickets and grasshoppers tuned to the
same frequency range (Stumpner and Nowotny 2014; Oldfield et al. 1986; Jacobs et al. 1999; Imaizumi and Pollack
1999), not unlike scotopic and photopic vision is adapted
to different light conditions. Finally, pooling over a wide
frequency band also improves reliability of envelope coding (Römer and Lewald 1992). The very limited number
of frequency bands is likely a response to the two major
goals of communication that is recognition of conspecifics
and avoidance of predators (Fig. 6). With the exception of
cicadas there may be little to gain in information by higher
spectral resolution as most signals display narrow or broad
band spectra and little frequency modulation. Instead, neural integration and lateral inhibition (Figs. 1, 6) may serve
for (1) channel separation in situations with mixed signals,
(2) distance coding or to boost (3) dynamic range of coding
for amplitude modulations and (4) improved reliability of
coding.
Conditioning signals for binaural processing
Computational roles for fine spectral resolution at the level
of sensory cells
The fact that higher frequency resolution at the hearing
organs rapidly collapses into very few processing channels brings up the question, whether early integration over
the audible spectrum leaves room for other computational
tasks. To date, cicadas represent the only group with distributed frequency sensitivity that could in principle
account for a finer spectral analysis than a categorical perception limited to two frequency channels (Fonseca et al.
2000). Cicadas are, therefore, equipped with the potential
ability to analyze the frequency-modulated songs known
for some, mostly tropical or sub-tropical species (Gogala
1995).
However, detailed spectral analysis may not be the only
advantage of across-spectrum integration. Notably, for tettigoniids the ability for frequency discrimination is given
by the differential tuning by an array of sensory cells.
However, the ability for spectral analysis is lost at higher
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The brains of insects are small and so are their bodies. Just
like small vertebrates, insects face the formidable task of
sound localization in the face of minute physical cues that
are available. Inter-aural time differences (ITDs) lie in the
microsecond range because of short distances between the
ears, inter-aural intensity differences are negligible considering the magnitude of a potential sound shadow (contribution Römer). Binaural processing in insects is dominated
by three computational motifs: a mechanical device sensitive to the incident angle of sound, contralateral inhibition
and parallel processing (Fig. 1).
Mechanical computation for binaural contrast
In insects, but also small vertebrates, a clever mechanical
device is implemented in the periphery that translates the
incident angle of sound into tympanic vibrations with large
amplitude differences. A pressure difference receiver is
inherently sensitive to the direction of incident sound, since
J Comp Physiol A (2015) 201:39–50
sound pressure acts at the outside and the inside of the
tympanum (Autrum 1942). The phase difference between
external and internal sound depends on the incident angle
of sound and the length of the internal pathway. These
phase differences are translated into amplitude differences
large enough for evaluation. By a comparison of binaural
response levels directional hearing in insects is possible
with very limited computational brain capacity. There are
large differences in the particular layout and position of
pressure difference receivers (Michelsen 1998; Miles et al.
1995), but nevertheless insects are equipped with directional information for further use by an apparatus that performs computations mechanically.
Neural enhancement of binaural contrast by contralateral
inhibition
The second important motif of computations for directional
hearing in insects is contralateral inhibition (Fig. 1). Most
prominently the omega-shaped interneurons of crickets
and katydids, but also the network structure of grasshoppers features contralateral projections (Wohlers and Huber
1982; Römer and Krusch 2000; von Helversen 1997).
Although the computational principle of contralateral inhibition is simple, its consequences are powerful. In crickets,
tettigoniids and grasshoppers, the contralateral suppression
serves contrast enhancement of directionality as evident
from first- or second-order interneurons (Pollack 1988;
Römer and Krusch 2000; Stumpner and Ronacher 1994).
Contralateral inhibition also creates acoustic hemispheres
for the animal known as selective attention (equivalent to
a simplified version of the cocktail party effect) observed
in first-order interneurons in crickets and tettigoniids (Pollack 1988; Römer and Krusch 2000; Marsat and Pollack
2005). By that, contralateral inhibition also serves figure
background discrimination, evident in the neuronal activity
of single neurons. Even more, contralateral inhibition may
eliminate delayed calling signals from the contralateral
processing pathway and thus a female’s sensory representation by suppression (Römer et al. 2002). This perceptual
selection for advanced calling with respect to other callers
demonstrates that a simple and local computational tool
can have ultimate consequences for female preference and
male signaling behavior.
47
et al. 1986; Stumpner and Ronacher 1994). The layout of
the peripheral network features contralateral inhibition and
excitation in separate pathways as evident from the directional sensitivity of interneurons (Ronacher and Stumpner
1993). However, for other groups of insects, evidence for
parallel processing is less clear. For crickets and tettigoniids also serial processing of pattern and direction was
proposed (Stabel et al. 1989; Schul et al. 1998, see however Poulet and Hedwig 2005). The directional cues then
do not originate from intensity differences alone, but also
from the quality differences of the signals as evaluated by
a downstream recognizer network (von Helversen and von
Helversen 1995). The single-channel pathways of lacewings, moths and mantids may not even require a separation of processing for pattern and direction, as the spectral
content at high intensity may carry sufficient object information by itself. For instance, moths and mantids exploit
the pulse rate sweep of an approaching bat to initiate an
evasive response (see contribution Pollack: hearing for
defense). Computationally, such a filter for pulse rate corresponds to a simple high pass and does not need to be intensity independent. Therefore, a separation of processing for
pattern and direction may not be required for these species.
In summary, a mechanical device relieves the auditory
pathway from computational strains and delivers inter-aural
intensity levels for processing. Contralateral inhibition
is the main computational tool for contrast enhancement
and enables acoustic hemispheres, selective attention and
object/background separation. Parallel processing of pattern and directional may separate cues, but it remains an
open question whether this separation is a general theme in
insect hearing or rather a specialty of few species.
Conclusion
The multitude of independently evolved auditory pathways
in insects offers unrivaled opportunity for the investigation
of computational principles (Fig. 1), since these may be
discussed separately from underlying mechanisms and may
reside upstream of species-specific recognition or classification steps. On the other hand, the processing demands
of species-specific recognition leave little room for general
processing themes as for instance required for visual or
auditory scene analysis in vertebrates.
Parallel processing pattern and directional information
Canonical processing themes
A third motif for the theme of directional hearing is parallel processing (Fig. 1). From a computational point of
view, processing capacities can then be devoted to specific
tasks and is not confronted with conflicting cues. It is well
described that grasshoppers separate pattern and directional
information into different processing streams (Ronacher
The control of sensitivity by means of adaptation in the
auditory periphery is one of the most ubiquitous themes in
peripheral processing of insects. However, adaptation may
ultimately serve very different goals, such as improvement
of the representation of relevant signals (Machens et al.
13
48
2003; Ronacher and Hennig 2004), mediating invariance
(Fig. 3a; Benda and Hennig 2008), and signal background
separation (Fig. 3b; Sobel and Tank 1994; Pollack 1988;
Wimmer et al. 2008). The recoding of a precise copy of a
sound’s amplitude modulation into an abstract representation of temporal features as described for the ascending
neurons of grasshopper is a fundamental computational
theme that is well known from vertebrate sensory systems (primary visual cortex: Vinje and Gallant 2000, auditory cortex: DeWeese et al. 2003, electrosensory system
of weakly electric fish: Vonderschen and Chacron 2011)
and also from the olfactory system of both vertebrates and
invertebrates (Laurent 2002). The example of the auditory
pathway of grasshopper also demonstrates the multifaceted
role of intrinsic noise in sensory processing that is worthwhile to study in more detail also in other insect taxa.
Dealing with noise is a common issue across all the
themes discussed here (Fig. 1). Noise reduction is possibly the most important role for frequency filtering and the
transformation of the coding scheme is a way to escape
neural signal degeneration (Fig. 6). This is not surprising: noise—external and internal—that degrades relevant
information is a common pressure for all insect auditory
systems. It may be one of the main constraints for the
evolution of early peripheral processing since insects lack
the highly redundant parallel processing streams that can
be found in most vertebrate auditory systems. Finally, we
show that both frequency filtering and binaural processing
are common themes across different species in insect audition; from a computational point of view both are general:
frequency filtering among other goals for noise reduction
and binaural processing for contrast enhancement.
Mechanisms and network layout underlying peripheral
processing
The detailed mechanisms underlying early peripheral processing may vary largely. For example, adaptation may be
mediated by different types of currents within single cells
(Benda and Herz 2003) but also by pre-synaptic inhibition (Hildebrandt et al. 2011) or calcium accumulation in
neurons (Sobel and Tank 1994; Baden and Hedwig 2006).
Binaural processing may be enhanced by both acoustic and
neural means and serves as another example for the same
operation enabled by different mechanisms.
A common ‘mechanism’ that may be often used in insect
audition may be the use physical rather than neural processing means. For both noise filtering (Fig. 6) and binaural
contrast enhancement, physical properties of the hearing
organs instead of neural implantations are used. The price
to pay for this layout may be that physically filtered information is not available for further neural processing. Early
filtering and purpose-driven code transformation may well
13
J Comp Physiol A (2015) 201:39–50
be one of the hallmarks of peripheral processing in insect
as compared to vertebrates. The small size of ears, bodies
and nervous systems of insects impose limitations that the
sensory periphery has to deal with. Massively redundant
parallel processing in vertebrates may impose different
requirements with respect to preservation of information
in the presence of noise, spectral processing and binaural
comparison.
The putatively most-abundant step of preprocessing,
sensitivity control and adaptation, requires some form of
feedback. This can be achieved by classical feedback loops
via pre-synaptic inhibition onto receptor axonal terminals
or by activity-dependent adaptation currents (Hildebrandt
et al. 2011)—both feeding back the output of a component
of a system to the input. Since sensitivity control may well
be the most general step in the auditory periphery, insect
pathways are strongly influenced by feedback and the
assumption of their feed-forward nature does not capture
one of their most important features. However, while local
feedback loops are observed, as for instance given by adaptation, larger feedback loops on a network level as known
from vertebrates and mammals, discussed in the context of
focused attention and Bayesian inference, seem not to be
present in the peripheral auditory pathways of insects.
In summary, even the vast diversity of insect auditory
pathways is governed by common themes and principles for
early auditory processing. Adaptation, recoding, frequency
filtering/integration and contralateral contrast enhancement
are recurring computational operations (Fig. 1). However,
not every species may use these operations to reach the
same goals, and the underlying mechanisms driving the
same operation are often not the same in different species.
Understanding the exact nature of the operations in different species may on the one hand help us in the understanding of the ultimate goal for processing of that species, but
can also reveal important hints for the investigation of the
auditory pathways of other species, insects and non-insects
alike.
Acknowledgments We thank Bernhard Ronacher for critical reading of the manuscript. This work was funded by a grant to MH and JB
by the Deutsche Forschungsgemeinschaft (SFB618).
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