Jan Clemens

Dynamic sensory cues shape song structure in Drosophila

The generation of acoustic communication signals is widespread across the animal kingdom, and males of many species, including Drosophilidae, produce patterned courtship songs to increase their chance of success with a female. For some animals, song structure can vary considerably from one rendition to the next; neural noise within pattern generating circuits is widely assumed to be the primary source of such variability, and statistical models that incorporate neural noise are successful at reproducing the full variation present in natural songs. In direct contrast, here we demonstrate that much of the pattern variability in Drosophila courtship song can be explained by taking into account the dynamic sensory experience of the male. In particular, using a quantitative behavioural assay combined with computational modelling, we find that males use fast modulations in visual and self-motion signals to pattern their songs, a relationship that we show is evolutionarily conserved. Using neural circuit manipulations, we also identify the pathways involved in song patterning choices and show that females are sensitive to song features. Our data not only demonstrate that Drosophila song production is not a fixed action pattern, but establish Drosophila as a valuable new model for studies of rapid decision-making under both social and naturalistic conditions.

Efficient transformation of an auditory population code in a small sensory system

Optimal coding principles are implemented in many large sensory systems. They include the systematic transformation of external stimuli into a sparse and decorrelated neuronal representation, enabling a flexible readout of stimulus properties. Are these principles also applicable to size-constrained systems, which have to rely on a limited number of neurons and may only have to fulfill specific and restricted tasks? We studied this question in an insect system—the early auditory pathway of grasshoppers. Grasshoppers use genetically fixed songs to recognize mates. The first steps of neural processing of songs take place in a small three-layer feed-forward network comprising only a few dozen neurons. We analyzed the transformation of the neural code within this network. Indeed, grasshoppers create a decorrelated and sparse representation, in accordance with optimal coding theory. Whereas the neuronal input layer is best read out as a summed population, a labeled-line population code for temporal features of the song is established after only two processing steps. At this stage, information about song identity is maximal for a population decoder that preserves neuronal identity. We conclude that optimal coding principles do apply to the early auditory system of the grasshopper, despite its size constraints. The inputs, however, are not encoded in a systematic, map-like fashion as in many larger sensory systems. Already at its periphery, part of the grasshopper auditory system seems to focus on behaviorally relevant features, and is in this property more reminiscent of higher sensory areas in vertebrates.

Time and timing in the acoustic recognition system of crickets

The songs of many insects exhibit precise timing as the result of repetitive and stereotyped subunits on several time scales. As these signals encode the identity of a species, time and timing are important for the recognition system that analyzes these signals. Crickets are a prominent example as their songs are built from sound pulses that are broadcast in a long trill or as a chirped song. This pattern appears to be analyzed on two timescales, short and long. Recent evidence suggests that song recognition in crickets relies on two computations with respect to time; a short linear-nonlinear (LN) model that operates as a filter for pulse rate and a longer integration time window for monitoring song energy over time. Therefore, there is a twofold role for timing. A filter for pulse rate shows differentiating properties for which the specific timing of excitation and inhibition is important. For an integrator, however, the duration of the time window is more important than the precise timing of events. Here, we first review evidence for the role of LN-models and integration time windows for song recognition in crickets. We then parameterize the filter part by Gabor functions and explore the effects of duration, frequency, phase, and offset as these will correspond to differently timed patterns of excitation and inhibition. These filter properties were compared with known preference functions of crickets and katydids. In a comparative approach, the power for song discrimination by LN-models was tested with the songs of over 100 cricket species. It is demonstrated how the acoustic signals of crickets occupy a simple 2-dimensional space for song recognition that arises from timing, described by a Gabor function, and time, the integration window. Finally, we discuss the evolution of recognition systems in insects based on simple sensory computations.

Nonlinear Computations Underlying Temporal and Population Sparseness in the Auditory System of the Grasshopper

Sparse coding schemes are employed by many sensory systems and implement efficient coding principles. Yet, the computations yielding sparse representations are often only partly understood. The early auditory system of the grasshopper produces a temporally and population-sparse representation of natural communication signals. To reveal the computations generating such a code, we estimated 1D and 2D linear-nonlinear models. We then used these models to examine the contribution of different model components to response sparseness. 2D models were better able to reproduce the sparseness measured in the system: while 1D models only captured 55% of the population sparseness at the networks output, 2D models accounted for 88% of it. Looking at the model structure, we could identify two types of computation, which increase sparseness. First, a sensitivity to the derivative of the stimulus and, second, the combination of a fast, excitatory and a slow, suppressive feature. Both were implemented in different classes of cells and increased the specificity and diversity of responses. The two types produced more transient responses and thereby amplified temporal sparseness. Additionally, the second type of computation contributed to population sparseness by increasing the diversity of feature selectivity through a wide range of delays between an excitatory and a suppressive feature. Both kinds of computation can be implemented through spike-frequency adaptation or slow inhibition—mechanisms found in many systems. Our results from the auditory system of the grasshopper are thus likely to reflect general principles underlying the emergence of sparse representations.

Sensorimotor Transformations Underlying Variability in Song Intensity during Drosophila Courtship

Diverse animal species, from insects to humans, utilize acoustic signals for communication. Studies of the neural basis for song or speech production have focused almost exclusively on the generation of spectral and temporal patterns, but animals can also adjust acoustic signal intensity when communicating. For example, humans naturally regulate the loudness of speech in accord with a visual estimate of receiver distance. The underlying mechanisms for this ability remain uncharacterized in any system. Here, we show that Drosophila males modulate courtship song amplitude with female distance, and we investigate each stage of the sensorimotor transformation underlying this behavior, from the detection of particular visual stimulus features and the timescales of sensory processing to the modulation of neural and muscle activity that generates song. Our results demonstrate an unanticipated level of control in insect acoustic communication and uncover novel computations and mechanisms underlying the regulation of acoustic signal intensity.

Computational principles underlying the recognition of acoustic signals in insects

Many animals produce pulse-like signals during acoustic communication. These signals exhibit structure on two time scales: they consist of trains of pulses that are often broadcast in packets—so called chirps. Temporal parameters of the pulse and of the chirp are decisive for female preference. Despite these signals being produced by animals from many different taxa (e.g. frogs, grasshoppers, crickets, bushcrickets, flies), a general framework for their evaluation is still lacking. We propose such a framework, based on a simple and physiologically plausible model. The model consists of feature detectors, whose time-varying output is averaged over the signal and then linearly combined to yield the behavioral preference. We fitted this model to large data sets collected in two species of crickets and found that Gabor filters—known from visual and auditory physiology—explain the preference functions in these two species very well. We further explored the properties of Gabor filters and found a systematic relationship between parameters of the filters and the shape of preference functions. Although these Gabor filters were relatively short, they were also able to explain aspects of the preference for signal parameters on the longer time scale due to the integration step in our model. Our framework explains a wide range of phenomena associated with female preference for a widespread class of signals in an intuitive and physiologically plausible fashion. This approach thus constitutes a valuable tool to understand the functioning and evolution of communication systems in many species.

Connecting Neural Codes with Behavior in the Auditory System of Drosophila

Brains are optimized for processing ethologically relevant sensory signals. However, few studies have characterized the neural coding mechanisms that underlie the transformation from natural sensory information to behavior. Here, we focus on acoustic communication in Drosophila melanogaster and use computational modeling to link natural courtship song, neuronal codes, and female behavioral responses to song. We show that melanogaster females are sensitive to long timescale song structure (on the order of tens of seconds). From intracellular recordings, we generate models that recapitulate neural responses to acoustic stimuli. We link these neural codes with female behavior by generating model neural responses to natural courtship song. Using a simple decoder, we predict female behavioral responses to the same song stimuli with high accuracy. Our modeling approach reveals how long timescale song features are represented by the Drosophila brain and how neural representations can be decoded to generate behavioral selectivity for acoustic communication signals.

Feature Extraction and Integration Underlying Perceptual Decision Making during Courtship Behavior

Traditionally, perceptual decision making is studied in trained animals and carefully controlled tasks. Here, we sought to elucidate the stimulus features and their combination underlying a naturalistic behavior—female decision making during acoustic courtship in grasshoppers. Using behavioral data, we developed a model in which stimulus features were extracted by physiologically plausible models of sensory neurons from the time-varying stimulus. This sensory evidence was integrated over the stimulus duration and combined to predict the behavior. We show that decisions were determined by the interaction of an excitatory and a suppressive stimulus feature. The observed increase of behavioral response with stimulus intensity was the result of an increase of the excitatory features gain that was not controlled by an equivalent increase of the suppressive feature. Differences in how these two features were combined could explain interindividual variability. In addition, the mapping between the two stimulus features and different parameters of the song led us to re-evaluate the cues underlying acoustic communication. Our framework provided a rich and plausible explanation of behavior in terms of two stimulus cues that were extracted by models of sensory neurons and combined through excitatory–inhibitory interactions. We thus were able to link single neurons feature selectivity and network computations with decision making in a natural task. This data-driven approach has the potential to advance our understanding of decision making in other systems and can inform the search for the neural correlates of behavior.

Asymmetrical integration of sensory information during mating decisions in grasshoppers

Significance Decision-making involves integrating different pieces of sensory information over time. Looking at how sensory information is weighted and integrated during a natural behavior can yield insight into the evolutionary forces shaping that behavior. Here, we investigated how female grasshoppers of the species Chorthippus biguttulus integrate information provided by male calling songs. Fitting a drift-diffusion model to behavioral data, we find that integration is highly asymmetrical: an unattractive song subunit far outweighs an attractive subunit. This asymmetrical integration is consistent with theories of sexual selection because it helps females avoid potentially costly interactions with unsuitable mating partners if the song belongs to another species or indicates a low-quality male; moreover, it suggests that song-based decision-making in grasshoppers is optimized by evolution. Decision-making processes, like all traits of an organism, are shaped by evolution; they thus carry a signature of the selection pressures associated with choice behaviors. The way sexual communication signals are integrated during courtship likely reflects the costs and benefits associated with mate choice. Here, we study the evaluation of male song by females during acoustic courtship in grasshoppers. Using playback experiments and computational modeling we find that information of different valence (attractive vs. nonattractive) is weighted asymmetrically: while information associated with nonattractive features has large weight, attractive features add little to the decision to mate. Accordingly, nonattractive features effectively veto female responses. Because attractive features have so little weight, the model suggests that female responses are frequently driven by integration noise. Asymmetrical weighting of negative and positive information may reflect the fitness costs associated with mating with a nonattractive over an attractive singer, which are also highly asymmetrical. In addition, nonattractive cues tend to be more salient and therefore more reliable. Hence, information provided by them should be weighted more heavily. Our findings suggest that characterizing the integration of sensory information during a natural behavior has the potential to provide valuable insights into the selective pressures shaping decision-making during evolution.

Acoustic duetting in Drosophila virilis relies on the integration of auditory and tactile signals

Many animal species, including insects, are capable of acoustic duetting, a complex social behavior in which males and females tightly control the rate and timing of their courtship song syllables relative to each other. The mechanisms underlying duetting remain largely unknown across model systems. Most studies of duetting focus exclusively on acoustic interactions, but the use of multisensory cues should aid in coordinating behavior between individuals. To test this hypothesis, we develop Drosophila virilis as a new model for studies of duetting. By combining sensory manipulations, quantitative behavioral assays, and statistical modeling, we show that virilis females combine precisely timed auditory and tactile cues to drive song production and duetting. Tactile cues delivered to the abdomen and genitalia play the larger role in females, as even headless females continue to coordinate song production with courting males. These data, therefore, reveal a novel, non-acoustic, mechanism for acoustic duetting. Finally, our results indicate that female-duetting circuits are not sexually differentiated, as males can also produce ‘female-like’ duets in a context-dependent manner. DOI: http://dx.doi.org/10.7554/eLife.07277.001