In this review, we discuss the emerging field of computational behavioral analysis-the use of modern methods from computer science and engineering to quantitatively measure animal behavior. We discuss aspects of experiment design important to both obtaining biologically relevant behavioral data and enabling the use of machine vision and learning techniques for automation. These two goals are often in conflict. Restraining or restricting the environment of the animal can simplify automatic behavior quantification, but it can also degrade the quality or alter important aspects of behavior. To enable biologists to design experiments to obtain better behavioral measurements, and computer scientists to pinpoint fruitful directions for algorithm improvement, we review known effects of artificial manipulation of the animal on behavior. We also review machine vision and learning techniques for tracking, feature extraction, automated behavior classification, and automated behavior discovery, the assumptions they make, and the types of data they work best with. Expected final online publication date for the Annual Review of Neuroscience Volume 39 is July 08, 2016. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

The brain is worthy of study because it is in charge of behavior. A flurry of recent technical advances in measuring and quantifying naturalistic behaviors provide an important opportunity for advancing brain science. However, the problem of understanding unrestrained behavior in the context of neural recordings and manipulations remains unsolved, and developing approaches to addressing this challenge is critical. Here we discuss considerations in computational neuroethology-the science of quantifying naturalistic behaviors for understanding the brain-and propose strategies to evaluate progress. We point to open questions that require resolution and call upon the broader systems neuroscience community to further develop and leverage measures of naturalistic, unrestrained behavior, which will enable us to more effectively probe the richness and complexity of the brain.

Mammalian cerebral cortex is accepted as being critical for voluntary motor control, but what functions depend on cortex is still unclear. Here we used rapid, reversible optogenetic inhibition to test the role of cortex during a head-fixed task in which mice reach, grab, and eat a food pellet. Sudden cortical inhibition blocked initiation or froze execution of this skilled prehension behavior, but left untrained forelimb movements unaffected. Unexpectedly, kinematically normal prehension occurred immediately after cortical inhibition even during rest periods lacking cue and pellet. This 'rebound' prehension was only evoked in trained and food-deprived animals, suggesting that a motivation-gated motor engram sufficient to evoke prehension is activated at inhibition's end. These results demonstrate the necessity and sufficiency of cortical activity for enacting a learned skill.

The motor cortex controls skilled arm movement by sending temporal patterns of activity to lower motor centres. Local cortical dynamics are thought to shape these patterns throughout movement execution. External inputs have been implicated in setting the initial state of the motor cortex, but they may also have a pattern-generating role. Here we dissect the contribution of local dynamics and inputs to cortical pattern generation during a prehension task in mice. Perturbing cortex to an aberrant state prevented movement initiation, but after the perturbation was released, cortex either bypassed the normal initial state and immediately generated the pattern that controls reaching or failed to generate this pattern. The difference in these two outcomes was probably a result of external inputs. We directly investigated the role of inputs by inactivating the thalamus; this perturbed cortical activity and disrupted limb kinematics at any stage of the movement. Activation of thalamocortical axon terminals at different frequencies disrupted cortical activity and arm movement in a graded manner. Simultaneous recordings revealed that both thalamic activity and the current state of cortex predicted changes in cortical activity. Thus, the pattern generator for dexterous arm movement is distributed across multiple, strongly interacting brain regions.

In this work, we address the problem of precisely localizing key frames of an action, for example, the precise time that a pitcher releases a baseball, or the precise time that a crowd begins to applaud. Key frame localization is a largely overlooked and important action-recognition problem, for example in the field of neuroscience, in which we would like to understand the neural activity that produces the start of a bout of an action. To address this problem, we introduce a novel structured loss function that properly weights the types of errors that matter in such applications: it more heavily penalizes extra and missed action start detections over small misalignments. Our structured loss is based on the best matching between predicted and labeled action starts. We train recurrent neural networks (RNNs) to minimize differentiable approximations of this loss. To evaluate these methods, we introduce the Mouse Reach Dataset, a large, annotated video dataset of mice performing a sequence of actions. The dataset was collected and labeled by experts for the purpose of neuroscience research. On this dataset, we demonstrate that our method outperforms related approaches and baseline methods using an unstructured loss.

To control reaching, the nervous system must generate large changes in muscle activation to drive the limb toward the target, and must also make smaller adjustments for precise and accurate behavior. Motor cortex controls the arm through projections to diverse targets across the central nervous system, but it has been challenging to identify the roles of cortical projections to specific targets. Here, we selectively disrupt cortico-cerebellar communication in the mouse by optogenetically stimulating the pontine nuclei in a cued reaching task. This perturbation did not typically block movement initiation, but degraded the precision, accuracy, duration, or success rate of the movement. Correspondingly, cerebellar and cortical activity during movement were largely preserved, but differences in hand velocity between control and stimulation conditions predicted from neural activity were correlated with observed velocity differences. These results suggest that while the total output of motor cortex drives reaching, the cortico-cerebellar loop makes small adjustments that contribute to the successful execution of this dexterous movement.

A general method to recognize and track unmarked animals within a population will enable new studies of social behavior and individuality.

Mice (Mus musculus) form large and dynamic social groups and emit ultrasonic vocalizations in a variety of social contexts. Surprisingly, these vocalizations have been studied almost exclusively in the context of cues from only one social partner, despite the observation that in many social species the presence of additional listeners changes the structure of communication signals. Here, we show that male vocal behavior elicited by female odor is affected by the presence of a male audience - with changes in vocalization count, acoustic structure and syllable complexity. We further show that single sensory cues are not sufficient to elicit this audience effect, indicating that multiple cues may be necessary for an audience to be apparent. Together, these experiments reveal that some features of mouse vocal behavior are only expressed in more complex social situations, and introduce a powerful new assay for measuring detection of the presence of social partners in mice.

The comprehensive reconstruction of cell lineages in complex multicellular organisms is a central goal of developmental biology. We present an open-source computational framework for the segmentation and tracking of cell nuclei with high accuracy and speed. We demonstrate its (i) generality by reconstructing cell lineages in four-dimensional, terabyte-sized image data sets of fruit fly, zebrafish and mouse embryos acquired with three types of fluorescence microscopes, (ii) scalability by analyzing advanced stages of development with up to 20,000 cells per time point at 26,000 cells min(-1) on a single computer workstation and (iii) ease of use by adjusting only two parameters across all data sets and providing visualization and editing tools for efficient data curation. Our approach achieves on average 97.0% linkage accuracy across all species and imaging modalities. Using our system, we performed the first cell lineage reconstruction of early Drosophila melanogaster nervous system development, revealing neuroblast dynamics throughout an entire embryo.

Hb9 is a homeodomain-containing transcription factor that acts in combination with Nkx6, Lim3, and Tail-up (Islet) to guide the stereotyped differentiation, connectivity, and function of a subset of neurons in Drosophila. The role of Hb9 in directing neuronal differentiation is well documented, but the lineage of Hb9(+) neurons is only partly characterized, its regulation is poorly understood, and most of the downstream genes through which it acts remain at large. Here, we complete the lineage tracing of all embryonic Hb9(+) neurons (to eight neuronal lineages) and provide evidence that hb9, lim3, and tail-up are coordinately regulated by a common set of upstream factors. Through the parallel use of micro-array gene expression profiling and the Dam-ID method, we searched for Hb9-regulated genes, uncovering transcription factors as the most over-represented class of genes regulated by Hb9 (and Nkx6) in the CNS. By a nearly ten-to-one ratio, Hb9 represses rather than activates transcription factors, highlighting transcriptional repression of other transcription factors as a core mechanism by which Hb9 governs neuronal determination. From the small set of genes activated by Hb9, we characterized the expression and function of two - fd59a/foxd, which encodes a transcription factor, and Nitric oxide synthase. Under standard lab conditions, both genes are dispensable for Drosophila development, but Nos appears to inhibit hyper-active behavior and fd59a appears to act in octopaminergic neurons to control egg-laying behavior. Together our data clarify the mechanisms through which Hb9 governs neuronal specification and differentiation and provide an initial characterization of the expression and function of Nos and fd59a in the Drosophila CNS.