Mapping model units to visual neurons reveals population code for social behaviour.

The rich variety of behaviours observed in animals arises through the interplay between sensory processing and motor control. To understand these sensorimotor transformations, it is useful to build models that predict not only neural responses to sensory input1-5 but also how each neuron causally contributes to behaviour6,7. Here we demonstrate a novel modelling approach to identify a one-to-one mapping between internal units in a deep neural network and real neurons by predicting the behavioural changes that arise from systematic perturbations of more than a dozen neuronal cell types. A key ingredient that we introduce is 'knockout training', which involves perturbing the network during training to match the perturbations of the real neurons during behavioural experiments. We apply this approach to model the sensorimotor transformations of Drosophila melanogaster males during a complex, visually guided social behaviour8-11. The visual projection neurons at the interface between the optic lobe and central brain form a set of discrete channels12, and prior work indicates that each channel encodes a specific visual feature to drive a particular behaviour13,14. Our model reaches a different conclusion: combinations of visual projection neurons, including those involved in non-social behaviours, drive male interactions with the female, forming a rich population code for behaviour. Overall, our framework consolidates behavioural effects elicited from various neural perturbations into a single, unified model, providing a map from stimulus to neuronal cell type to behaviour, and enabling future incorporation of wiring diagrams of the brain15 into the model.

Experimental and statistical reevaluation provides no evidence for Drosophila courtship song rhythms.

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 previously performed automated segmentation of songs but failed to detect the proposed rhythm [Arthur BJ, et al. (2013) BMC Biol 11:11; Stern DL (2014) BMC Biol 12:38]. Kyriacou et al. [Kyriacou CP, et al. (2017) Proc Natl Acad Sci USA 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 dataset provide no evidence for either the existence of song rhythms or song periodicity differences between genotypes. Furthermore, we have reexamined 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 no evidence for the existence of Drosophila courtship song rhythms.

Everyone knows what behavior is but they just don't agree on it.

Studying "behavior" lies at the heart of many disciplines. Nevertheless, academics rarely provide an explicit definition of what "behavior" actually is. What range of definitions do people use, and how does that vary across disciplines? To answer these questions, we have developed a survey to probe what constitutes "behavior." We find that academics adopt different definitions of behavior according to their academic discipline, animal model that they work with, and level of academic seniority. Using hierarchical clustering, we identify at least six distinct types of "behavior" which are used in seven distinct operational archetypes of "behavior." Individual respondents have clear consistent definitions of behavior, but these definitions are not consistent across the population. Our study is a call for academics to clarify what they mean by "behavior" wherever they study it, with the hope that this will foster interdisciplinary studies that will improve our understanding of behavioral phenomena.

Author Correction: Unsupervised identification of the internal states that shape natural behavior.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The neural basis for a persistent internal state in Drosophila females.

Sustained changes in mood or action require persistent changes in neural activity, but it has been difficult to identify the neural circuit mechanisms that underlie persistent activity and contribute to long-lasting changes in behavior. Here, we show that a subset of Doublesex+ pC1 neurons in the Drosophila female brain, called pC1d/e, can drive minutes-long changes in female behavior in the presence of males. Using automated reconstruction of a volume electron microscopic (EM) image of the female brain, we map all inputs and outputs to both pC1d and pC1e. This reveals strong recurrent connectivity between, in particular, pC1d/e neurons and a specific subset of Fruitless+ neurons called aIPg. We additionally find that pC1d/e activation drives long-lasting persistent neural activity in brain areas and cells overlapping with the pC1d/e neural network, including both Doublesex+ and Fruitless+ neurons. Our work thus links minutes-long persistent changes in behavior with persistent neural activity and recurrent circuit architecture in the female brain.

Long-term mental states such as arousal and mood variations rely on persistent changes in the activity of certain neural circuits which have been difficult to identify. For instance, in male fruit flies, the activation of a particular circuit containing 'P1 neurons' can escalate aggressive and mating behaviors. However, less is known about the neural networks that underlie arousal in female flies. A group of female-specific, 'pC1 neurons' similar to P1 neurons could play this role, but it was unclear whether it could drive lasting changes in female fly behavior. To investigate this question, Deutsch et al. stimulated or shut down pC1 circuits in female flies, and then recorded the insects' interactions with male flies. Stimulation was accomplished using optogenetics, a technique which allows researchers to precisely control the activity of specially modified light-sensitive neurons. Silencing pC1 neurons in female flies diminished their interest in male partners and their suitor's courtship songs. Activating these neural circuits made the females more receptive to males; it also triggered long-lasting aggressive behaviors not typically observed in virgin females, such as shoving and chasing. Deutsch et al. then identified the brain cells that pC1 neurons connect to, discovering that these neurons are part of an interconnected circuit also formed of aIPg neurons ­ a population of fly brain cells that shows sex differences and is linked to female aggression. The brains of females were then imaged as pC1 neurons were switched on, revealing a persistent activity which outlasted the activation in circuits containing both pC1 and aIPg neurons. Thus, these results link neural circuit architecture to long lasting changes in neural activity, and ultimately, in behavior. Future experiments can build on these results to determine how this circuit is activated during natural social interactions.

Unsupervised identification of the internal states that shape natural behavior.

Internal states shape stimulus responses and decision-making, but we lack methods to identify them. To address this gap, we developed an unsupervised method to identify internal states from behavioral data and applied it to a dynamic social interaction. During courtship, Drosophila melanogaster males pattern their songs using feedback cues from their partner. Our model uncovers three latent states underlying this behavior and is able to predict moment-to-moment variation in song-patterning decisions. These states correspond to different sensorimotor strategies, each of which is characterized by different mappings from feedback cues to song modes. We show that a pair of neurons previously thought to be command neurons for song production are sufficient to drive switching between states. Our results reveal how animals compose behavior from previously unidentified internal states, which is a necessary step for quantitative descriptions of animal behavior that link environmental cues, internal needs, neuronal activity and motor outputs.

Quantifying behavior to solve sensorimotor transformations: advances from worms and flies.

The development of new computational tools has recently opened up the study of natural behaviors at a precision that was previously unachievable. These tools permit a highly quantitative analysis of behavioral dynamics at timescales that are well matched to the timescales of neural activity. Here we examine how combining these methods with established techniques for estimating an animal's sensory experience presents exciting new opportunities for dissecting the sensorimotor transformations performed by the nervous system. We focus this review primarily on examples from Caenorhabditis elegans and Drosophila melanogaster-for these model systems, computational approaches to characterize behavior, in combination with unparalleled genetic tools for neural activation, silencing, and recording, have already proven instrumental for illuminating underlying neural mechanisms.

Neural Mechanisms for Evaluating Environmental Variability in Caenorhabditis elegans.

The ability to evaluate variability in the environment is vital for making optimal behavioral decisions. Here we show that Caenorhabditis elegans evaluates variability in its food environment and modifies its future behavior accordingly. We derive a behavioral model that reveals a critical period over which information about the food environment is acquired and predicts future search behavior. We also identify a pair of high-threshold sensory neurons that encode variability in food concentration and the downstream dopamine-dependent circuit that generates appropriate search behavior upon removal from food. Further, we show that CREB is required in a subset of interneurons and determines the timescale over which the variability is integrated. Interestingly, the variability circuit is a subset of a larger circuit driving search behavior, showing that learning directly modifies the very same neurons driving behavior. Our study reveals how a neural circuit decodes environmental variability to generate contextually appropriate decisions.

Maximally informative foraging by Caenorhabditis elegans.

Animals have evolved intricate search strategies to find new sources of food. Here, we analyze a complex food seeking behavior in the nematode Caenorhabditis elegans (C. elegans) to derive a general theory describing different searches. We show that C. elegans, like many other animals, uses a multi-stage search for food, where they initially explore a small area intensively ('local search') before switching to explore a much larger area ('global search'). We demonstrate that these search strategies as well as the transition between them can be quantitatively explained by a maximally informative search strategy, where the searcher seeks to continuously maximize information about the target. Although performing maximally informative search is computationally demanding, we show that a drift-diffusion model can approximate it successfully with just three neurons. Our study reveals how the maximally informative search strategy can be implemented and adopted to different search conditions.

Information theory of adaptation in neurons, behavior, and mood.

The ability to make accurate predictions of future stimuli and consequences of one's actions are crucial for the survival and appropriate decision-making. These predictions are constantly being made at different levels of the nervous system. This is evidenced by adaptation to stimulus parameters in sensory coding, and in learning of an up-to-date model of the environment at the behavioral level. This review will discuss recent findings that actions of neurons and animals are selected based on detailed stimulus history in such a way as to maximize information for achieving the task at hand. Information maximization dictates not only how sensory coding should adapt to various statistical aspects of stimuli, but also that reward function should adapt to match the predictive information from past to future.