Eyeblink conditioning in restrained rabbits has served as an excellent model of cerebellar-dependent motor learning for many decades. In mice, the role of the cerebellum in eyeblink conditioning is less clear and remains controversial, partly because learning appears to engage fear-related circuits and lesions of the cerebellum do not abolish the learned behavior completely. Furthermore, experiments in mice are performed using freely moving systems, which lack the stability necessary for mapping out the essential neural circuitry with electrophysiological approaches. We have developed a novel apparatus for eyeblink conditioning in head-fixed mice. Here, we show that the performance of mice in our apparatus is excellent and that the learned behavior displays two hallmark features of cerebellardependent eyeblink conditioning in rabbits: (1) gradual acquisition; and (2) adaptive timing of conditioned movements. Furthermore, we use a combination of pharmacological inactivation, electrical stimulation, single-unit recordings, and targeted microlesions to demonstrate that the learned behavior is completely dependent on the cerebellum and to pinpoint the exact location in the deep cerebellar nuclei that is necessary. Our results pave the way for using eyeblink conditioning in head-fixed mice as a platform for applying next-generation genetic tools to address molecular and circuit-level questions about cerebellar function in health and disease.

Finding sought visual targets requires our brains to flexibly combine working memory information about what we are looking for with visual information about what we are looking at. To investigate the neural computations involved in finding visual targets, we recorded neural responses in inferotemporal (IT) and perirhinal (PRH) cortex as macaque monkeys performed a task that required them to find targets within sequences of distractors. We found similar amounts of total task-specific information in both areas, however, information about whether a target was in view was more accessible using a linear read-out (i.e. was more “untangled”) in PRH. Consistent with the flow of information from IT to PRH, we also found that task-relevant information arrived earlier in IT. PRH responses were well-described by a functional model in which “untangling” computations in PRH reformat input from IT by combining neurons with asymmetric tuning correlations for target matches and distractors.

Inter-male aggressive behavior is a prominent sexually dimorphic behavior. Neural circuits that underlie aggressive behavior are therefore likely under the control of sex-determining genes. However, the neurogenetic mechanism that generates sex-specific aggressive behavior remains largely unknown. Here, we found that a neuronal class specified by one of the Drosophila sex determining genes, fruitless (fru), belongs to the neural circuit that generates male-type aggressive behavior. This neuronal class can promote aggressive behavior independent of another sex determining gene, doublesex (dsx), although dsx is involved in ensuring that aggressive behavior is performed only toward males. We also found that three fru isoforms with different DNA binding domains show a division of labor on male aggressive behaviors. A dominant role of fru in specifying sex-specific aggressive behavior may underscore a genetic mechanism that allows male-type aggressive behavior to evolve at least partially independently from courtship behavior, which is under different selective pressures.

For successful mating, a male animal must execute effective courtship behaviors toward a receptive target sex, which is female. Whether the courtship execution capability and upregulation of courtship toward females are specified through separable sex-determining genetic pathways remains uncharacterized. Here, we found that one of the two Drosophila sex-determining genes, doublesex (dsx), specifies a male-specific neuronal component that serves as an execution mechanism for courtship behavior, whereas fruitless (fru) is required for enhancement of courtship behavior toward females. The dsx-dependent courtship execution mechanism includes a specific subclass within a neuronal cluster that co-express dsx and fru. This cluster contains at least another subclass that is specified cooperatively by both dsx and fru. Although these neuronal populations can also promote aggressive behavior toward male flies, this capacity requires fru-dependent mechanisms. Our results uncover how sex-determining genes specify execution capability and female-specific enhancement of courtship behavior through separable yet cooperative neurogenetic mechanisms.

Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. Such steps include the design of the algorithm for machine learning, the methods used for animal tracking, the choice of training images, and the benchmarking of classification outcomes. However, how these design choices contribute to the interpretation of automated behavioral classifications has not been extensively characterized. Here, we quantify the effects of experimenter choices on the outputs of automated classifiers of Drosophila social behaviors. Drosophila behaviors contain a considerable degree of variability, which was reflected in the confidence levels associated with both human and computer classifications. We found that a diversity of sex combinations and tracking features was important for robust performance of the automated classifiers. In particular, features concerning the relative position of flies contained useful information for training a machine-learning algorithm. These observations shed light on the importance of human influence on tracking algorithms, the selection of training images, and the quality of annotated sample images used to benchmark the performance of a classifier (the ‘ground truth’). Evaluation of these factors is necessary for researchers to accurately interpret behavioral data quantified by a machine-learning algorithm and to further improve automated classifications.

Neuropeptides influence animal behaviors through complex molecular and cellular mechanisms, the physiological and behavioral effects of which are difficult to predict solely from synaptic connectivity. Many neuropeptides can activate multiple receptors, whose ligand affinity and downstream signaling cascades are often different from one another. Although we know that the diverse pharmacological characteristics of neuropeptide receptors form the basis of unique neuromodulatory effects on distinct downstream cells, it remains unclear exactly how different receptors shape the downstream activity patterns triggered by a single neuronal neuropeptide source. Here, we uncovered two separate downstream targets that are differentially modulated by tachykinin, an aggression-promoting neuropeptide inDrosophila. Tachykinin from a single male-specific neuronal type recruits two separate downstream groups of neurons. One downstream group, synaptically connected to the tachykinergic neurons, expresses the receptorTkR86Cand is necessary for aggression. Here, tachykinin supports cholinergic excitatory synaptic transmission between the tachykinergic andTkR86Cdownstream neurons. The other downstream group expresses theTkR99Dreceptor and is recruited primarily when tachykinin is over-expressed in the source neurons. Differential activity patterns in the two groups of downstream neurons correlate with levels of male aggression triggered by the tachykininergic neurons. These findings highlight how the amount of neuropeptide released from a small number of neurons can reshape the activity patterns of multiple downstream neuronal populations. Our results lay the foundation for further investigations into the neurophysiological mechanism by which a neuropeptide controls complex behaviors.SIGNIFICANCE STATEMENT:Neuropeptides control a variety of innate behaviors, including social behaviors, in both animals and humans. Unlike fast-acting neurotransmitters, neuropeptides can elicit distinct physiological responses in different downstream neurons. How such diverse physiological effects coordinate complex social interactions remains unknown. This study uncovers the firstin vivoexample of a neuropeptide from a single neuronal source eliciting distinct physiological responses in multiple downstream neurons that express different neuropeptide receptors. Understanding the unique motif of neuropeptidergic modulation, which may not be easily predicted from a synaptic connectivity map, can help elucidate how neuropeptides orchestrate complex behaviors by modulating multiple target neurons simultaneously.

Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. Such steps include the design of the algorithm for machine learning, the methods used for animal tracking, the choice of training images, and the benchmarking of classification outcomes. However, how these design choices contribute to the interpretation of automated behavioral classifications has not been extensively characterized. Here, we quantify the effects of experimenter choices on the outputs of automated classifiers of Drosophila social behaviors. Drosophila behaviors contain a considerable degree of variability, which was reflected in the confidence levels associated with both human and computer classifications. We found that a diversity of sex combinations and tracking features was important for robust performance of the automated classifiers. In particular, features concerning the relative position of flies contained useful information for training a machine-learning algorithm. These observations shed light on the importance of human influence on tracking algorithms, the selection of training images, and the quality of annotated sample images used to benchmark the performance of a classifier (the 'ground truth'). Evaluation of these factors is necessary for researchers to accurately interpret behavioral data quantified by a machine-learning algorithm and to further improve automated classifications. Significance Statement:Accurate quantification of animal behaviors is fundamental to neuroscience. Here, we quantitatively assess how human choices influence the performance of automated classifiers trained by a machine-learning algorithm. We found that human decisions about the computational tracking method, the training images, and the images used for performance evaluation impact both the classifier outputs and how human observers interpret the results. These factors are sometimes overlooked but are critical, especially because animal behavior is itself inherently variable. Automated quantification of animal behavior is becoming increasingly prevalent: our results provide a model for bridging the gap between traditional human annotations and computer-based annotations. Systematic assessment of human choices is important for developing behavior classifiers that perform robustly in a variety of experimental conditions. 3

Across diverse insect taxa, the behavior and physiology of females dramatically changes after mating – processes largely triggered by the transfer of seminal proteins from their mates. In the vinegar flyDrosophila melanogaster, the seminal protein sex peptide (SP) decreases the likelihood of female flies remating and causes additional behavioral and physiological changes that promote fertility including increasing egg production. Although SP is only found in theDrosophilagenus, its receptor, sex peptide receptor (SPR), is the widely-conserved myoinhibitory peptide (MIP) receptor. To test the functional role of SPR in mediating post-mating responses in a non-Drosophiladipteran, we generated two independentSpr-knockout alleles in the yellow fever mosquitoAedes aegypti. Although SPR is needed for post-mating responses inDrosophilaand the cotton bollwormHelicoverpa armigera,SprmutantAe. aegyptishow completely normal post-mating decreases in remating propensity and increases in egg laying. In addition, injection of synthetic SP or accessory gland homogenate fromD. melanogasterinto virgin female mosquitoes did not elicit these post-mating responses. Our results indicate thatSpris not required for these canonical post-mating responses inAe. aegypti, indicating that unknown signaling pathways are likely responsible for these behavioral switches in this disease vector.