Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
It has been proposed that a single sniff generates a "snapshot" of the olfactory world. However, odor coding on this timescale is poorly understood, and it is not known whether coding is invariant to changes in respiration frequency. We investigated this by recording spike trains from the olfactory bulb in awake, behaving rats. During rapid sniffing, odor inhalation triggered rapid and reliable cell- and odor-specific temporal spike patterns. These fine temporal responses conveyed substantial odor information within the first ∼100 ms, and correlated with behavioral discrimination time on a trial-by-trial basis. Surprisingly, the initial transient portions of responses were highly conserved between rapid sniffing and slow breathing. Firing rates over the entire respiration cycle carried less odor information, did not correlate with behavior, and were poorly conserved across respiration frequency. These results suggest that inhalation-coupled transient activity forms a robust neural code that is invariant to changes in respiration behavior.
Egg-laying behavior is one of the most important aspects of female behavior, and has a profound impact on the fitness of a species. As such, it is controlled by several layers of regulation. Here, we review recent advances in our understanding of insect neural circuits that control when, where and how to lay an egg. We also outline outstanding open questions about the control of egg-laying decisions, and speculate on the possible neural underpinnings that can drive the diversification of oviposition behaviors through evolution.
Innate behaviors are frequently comprised of ordered sequences of component actions that progress to satisfy essential drives. Progression is governed by specialized sensory cues that induce transitions between components within the appropriate context. Here we have characterized the structure of the egg-laying behavioral sequence in Drosophila and found significant variability in the transitions between component actions that affords the organism an adaptive flexibility. We identified distinct classes of interoceptive and exteroceptive sensory neurons that control the timing and direction of transitions between the terminal components of the sequence. We also identified a pair of motor neurons that enact the final transition to egg expulsion. These results provide a logic for the organization of innate behavior in which sensory information processed at critical junctures allows for flexible adjustments in component actions to satisfy drives across varied internal and external environments.
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