Biology subjects

Murthy, M.

Publications and source records attributed to Murthy, M..

4 recordsLinked to original sources

bioRxiv2018

Shared Song Object Detector Neurons in Drosophila Male and Female Brains Drive Divergent, Sex-Specific Behaviors

Males and females often produce distinct responses to the same sensory stimuli. How such differences arise - at the level of sensory processing or in the circuits that generate behavior - remains largely unresolved across sensory modalities. We address this issue in the acoustic communication system of Drosophila. During courtship, males generate time-varying songs, and each sex responds with specific behaviors. We characterize male and female behavioral tuning for all aspects of song, and show that feature tuning is similar between sexes, suggesting sex-shared song detectors drive divergent behaviors. We then identify higher-order neurons in the Drosophila brain, called pC2, that are tuned for multiple temporal aspects of one mode of the males song, and drive sex-specific behaviors. We thus uncover neurons that are specifically tuned to an acoustic communication signal and that reside at the sensory-motor interface, flexibly linking auditory perception with sex-specific behavioral responses.

neuroscience↗

bioRxiv2018

Fast animal pose estimation using deep neural networks

Recent work quantifying postural dynamics has attempted to define the repertoire of behaviors performed by an animal. However, a major drawback to these techniques has been their reliance on dimensionality reduction of images which destroys information about which parts of the body are used in each behavior. To address this issue, we introduce a deep learning-based method for pose estimation, LEAP (LEAP Estimates Animal Pose). LEAP automatically predicts the positions of animal body parts using a deep convolutional neural network with as little as 10 frames of labeled data for training. This framework consists of a graphical interface for interactive labeling of body parts and software for training the network and fast prediction on new data (1 hr to train, 185 Hz predictions). We validate LEAP using videos of freely behaving fruit flies (Drosophila melanogaster) and track 32 distinct points on the body to fully describe the pose of the head, body, wings, and legs with an error rate of <3% of the animals body length. We recapitulate a number of reported findings on insect gait dynamics and show LEAPs applicability as the first step in unsupervised behavioral classification. Finally, we extend the method to more challenging imaging situations (pairs of flies moving on a mesh-like background) and movies from freely moving mice (Mus musculus) where we track the full conformation of the head, body, and limbs.

animal behavior and cognition↗

bioRxiv2017

Fast Intensity Adaptation Enhances the Encoding of Sound in Drosophila

To faithfully encode complex stimuli, sensory neurons should correct, via adaptation, for stimulus properties that corrupt pattern recognition. Here, we investigate sound intensity adaptation in the Drosophila auditory system, which is largely devoted to processing courtship song. Mechanosensory neurons (JONs) in the antenna are sensitive not only to sound-induced antennal vibrations, but also to wind or gravity, which affect the antennas mean position. Song pattern recognition therefore requires adaptation to antennal position (stimulus mean) in addition to sound intensity (stimulus variance). We discover fast variance adaptation in Drosophila JONs, which corrects for background noise over the behaviorally relevant intensity range. We determine where mean and variance adaptation arises and how they interact. A computational model explains our results using a sequence of subtractive and divisive adaptation modules, interleaved by rectification. These results lay the foundation for identifying the molecular and biophysical implementation of adaptation to the statistics of natural sensory stimuli.

neuroscience↗

bioRxiv2017

Reported Drosophila Courtship Song Rhythms Remain Data Analysis Artifacts

From 1980 to 1992, a series of influential papers reported on the discovery, genetics, and evolution of a periodic cycling of the interval between Drosophila male courtship song pulses. The molecular mechanisms underlying this periodicity were never described. To reinitiate investigation of this phenomenon, we performed automated segmentation of songs, but failed to detect the proposed periodicity [Arthur BJ et al. (2013) BMC Biol 11:11; Stern DL (2014) BMC Biol 12:38]. Kyriacou CP et al. [(2017) PNAS 114:1970-1975] report that we failed to detect song rhythms because i) our flies did not sing enough and ii) our segmenter did not identify many of the song pulses. Kyriacou et al. manually annotated a subset of our recordings and reported that two strains displayed rhythms with genotype-specific periodicity, in agreement with their original reports. We cannot replicate this finding and show that the manually-annotated data, the original automatically segmented data, and a new data set provide no evidence for either the existence of song rhythms or song periodicity differences between genotypes. Furthermore, we have re-examined our methods and analysis and find that our automated segmentation method was not biased to prevent detection of putative song periodicity. We conclude that there is currently no evidence for the existence of Drosophila courtship song rhythms.\n\nSignificance statementPrevious studies have reported that male vinegar flies sing courtship songs with a periodic rhythm of approximately 55 seconds. Several years ago, we showed that we could not replicate this observation. Recently, the original authors have claimed that we failed to find rhythms because 1) our flies did not sing enough and 2) our software for detecting song did not detect all song events. They claimed that they could detect rhythms in song annotated by hand. We report here that we cannot replicate their observation of rhythms in the hand-annotated data or in any dataset and that our original methods were not biased against detecting rhythms. We conclude that song rhythms cannot be detected.

animal behavior and cognition↗