@article {69137, title = {Anatomically distributed neural representations of instincts in the hypothalamus.}, journal = {bioRxiv}, year = {2023}, month = {2023 Dec 07}, abstract = {

Artificial activation of anatomically localized, genetically defined hypothalamic neuron populations is known to trigger distinct innate behaviors, suggesting a hypothalamic nucleus-centered organization of behavior control. To assess whether the encoding of behavior is similarly anatomically confined, we performed simultaneous neuron recordings across twenty hypothalamic regions in freely moving animals. Here we show that distinct but anatomically distributed neuron ensembles encode the social and fear behavior classes, primarily through mixed selectivity. While behavior class-encoding ensembles were spatially distributed, individual ensembles exhibited strong localization bias. Encoding models identified that behavior actions, but not motion-related variables, explained a large fraction of hypothalamic neuron activity variance. These results identify unexpected complexity in the hypothalamic encoding of instincts and provide a foundation for understanding the role of distributed neural representations in the expression of behaviors driven by hardwired circuits.

}, doi = {10.1101/2023.11.21.568163}, url = {https://doi.org/10.1101/2023.11.21.568163}, author = {Stagkourakis, Stefanos and Spigolon, Giada and Marks, Markus and Feyder, Michael and Kim, Joseph and Perona, Pietro and Pachitariu, Marius and Anderson, David J} } @article {65179, title = {Computational neuroethology: A call to action.}, journal = {Neuron}, volume = {104}, year = {2019}, month = {2019 Oct 09}, pages = {11-24}, abstract = {

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.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2019.09.038}, author = {Datta, Sandeep Robert and Anderson, David J and Branson, Kristin and Perona, Pietro and Leifer, Andrew} } @article {48005, title = {Learning recurrent representations for hierarchical behavior modeling.}, journal = {arXiv}, volume = {arXiv:1611.00094}, year = {2016}, month = {2016 Nov 1}, abstract = {

We propose a framework for detecting action patterns from motion sequences and modeling the sensory-motor relationship of animals, using a generative recurrent neural network. The network has a discriminative part (classifying actions) and a generative part (predicting motion), whose recurrent cells are laterally connected, allowing higher levels of the network to represent high level phenomena. We test our framework on two types of data, fruit fly behavior and online handwriting. Our results show that 1) taking advantage of unlabeled sequences, by predicting future motion, significantly improves action detection performance when training labels are scarce, 2) the network learns to represent high level phenomena such as writer identity and fly gender, without supervision, and 3) simulated motion trajectories, generated by treating motion prediction as input to the network, look realistic and may be used to qualitatively evaluate whether the model has learnt generative control rules.

}, author = {Eyjolfsdottir, Eyrun and Branson, Kristin and Yue, Yisong and Perona, Pietro} } @article {46753, title = {Behavioral responses to a repetitive visual threat stimulus express a persistent state of defensive arousal in Drosophila}, journal = {Current Biology}, volume = {25}, year = {2015}, month = {2015 May 14}, pages = {1401-15}, abstract = {

The neural circuit mechanisms underlying emotion states remain poorly understood. Drosophila offers powerful genetic approaches for dissecting neural circuit function, but whether flies exhibit emotion-like behaviors has not been clear. We recently proposed that model organisms may express internal states displaying {\textquotedblleft}emotion primitives,{\textquotedblright} which are general characteristics common to different emotions, rather than specific anthropomorphic emotions such as {\textquotedblleft}fear{\textquotedblright} or {\textquotedblleft}anxiety.{\textquotedblright} These emotion primitives include scalability, persistence, valence, and generalization to multiple contexts. Here, we have applied this approach to determine whether flies{\textquoteright} defensive responses to moving overhead translational stimuli ({\textquotedblleft}shadows{\textquotedblright}) are purely reflexive or may express underlying emotion states. We describe a new behavioral assay in which flies confined in an enclosed arena are repeatedly exposed to an overhead translational stimulus. Repetitive stimuli promoted graded (scalable) and persistent increases in locomotor velocity and hopping, and occasional freezing. The stimulus also dispersed feeding flies from a food resource, suggesting both negative valence and context generalization. Strikingly, there was a significant delay before the flies returned to the food following stimulus-induced dispersal, suggestive of a slowly decaying internal defensive state. The length of this delay was increased when more stimuli were delivered for initial dispersal. These responses can be mathematically modeled by assuming an internal state that behaves as a leaky integrator of stimulus exposure. Our results suggest that flies{\textquoteright} responses to repetitive visual threat stimuli express an internal state exhibiting canonical emotion primitives, possibly analogous to fear in mammals. The mechanistic basis of this state can now be investigated in a genetically tractable insect species.

}, doi = {10.1016/j.cub.2015.03.058}, author = {Gibson, William T. and Gonzalez, Carlos R. and Fernandez, Conchi and Ramasamy, Lakshminarayanan and Tabachnik, Tanya and Du, Rebecca R. and Felsen, Panna D. and Maire, Michael M. and Perona, Pietro and Anderson, David J.} } @article {45098, title = {Automated image-based tracking and its application in ecology.}, journal = {Trends in Ecology and Evolution}, volume = {29}, year = {2014}, month = {2014 Jul}, pages = {417-428}, abstract = {

The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers.

}, issn = {1872-8383}, doi = {10.1016/j.tree.2014.05.004}, author = {Dell, Anthony I and Bender, John A and Branson, Kristin and Couzin, Iain D and de Polavieja, Gonzalo G and Noldus, Lucas P J J and P{\'e}rez-Escudero, Alfonso and Perona, Pietro and Straw, Andrew D and Wikelski, Martin and Brose, Ulrich} } @article {6148, title = {Automated multi-day tracking of marked mice for the analysis of social behaviour.}, journal = {Journal of Neuroscience Methods}, volume = {219}, year = {2013}, month = {2013 Sep 30}, pages = {10-19}, abstract = {

A quantitative description of animal social behaviour is informative for behavioural biologists and clinicians developing drugs to treat social disorders. Social interaction in a group of animals has been difficult to measure because behaviour develops over long periods of time and requires tedious manual scoring, which is subjective and often non-reproducible. Computer-vision systems with the ability to measure complex social behaviour automatically would have a transformative impact on biology. Here, we present a method for tracking group-housed mice individually as they freely interact over multiple days. Each mouse is bleach-marked with a unique fur pattern. The patterns are automatically learned by the tracking software and used to infer identities. Trajectories are analysed to measure behaviour as it develops over days, beyond the range of acute experiments. We demonstrate how our system may be used to study the development of place preferences, associations and social relationships by tracking four mice continuously for five days. Our system enables accurate and reproducible characterisation of wild-type mouse social behaviour and paves the way for high-throughput long-term observation of the effects of genetic, pharmacological and environmental manipulations.

}, issn = {1872-678X}, doi = {10.1016/j.jneumeth.2013.05.013}, author = {Ohayon, Shay and Avni, Ofer and Taylor, Adam L and Perona, Pietro and Roian Egnor, S E} } @proceedings {5767, title = {Learning animal social behavior from trajectory features.}, year = {2012}, month = {2012 Nov}, address = {Tsukuba, Japan}, author = {Eyjolfsdottir, Eyrun and Burgos-Artizzu, Xavier P. and Branson, Steven and Branson, Kristin and Anderson, David and Perona, Pietro} } @article {5036, title = {A simple strategy for detecting moving objects during locomotion revealed by animal-robot interactions.}, journal = {Current Biology}, volume = {22}, year = {2012}, month = {2012 Jun 20}, pages = {1344-50}, abstract = {

An important role of visual systems is to detect nearby predators, prey, and potential mates [1], which may be distinguished in part by their motion. When an animal is at rest, an object moving in any direction may easily be detected by motion-sensitive visual circuits [2, 3]. During locomotion,\ however, this strategy is compromised because the observer must detect a moving object within the pattern of\ optic flow created by its own motion through the stationary\ background. However, objects that move creating back-to-front (regressive) motion may be unambiguously distinguished from stationary objects because forward locomotion creates only front-to-back (progressive) optic flow. Thus, moving animals should exhibit an enhanced sensitivity to regressively moving objects. We explicitly tested this hypothesis by constructing a simple fly-sized robot that was programmed to interact with a real fly. Our measurements indicate that whereas walking female flies freeze in response to a regressively moving object, they ignore a progressively moving one. Regressive motion salience also explains observations of behaviors exhibited by pairs of walking flies. Because the assumptions underlying the regressive motion salience hypothesis are general, we suspect that the behavior we have observed in Drosophila may be widespread among eyed, motile organisms.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2012.05.024}, author = {Zabala, Francisco and Polidoro, Peter and Robie, Alice A and Branson, Kristin and Perona, Pietro and Dickinson, Michael H} } @article {2678, title = {Automated monitoring and analysis of social behavior in Drosophila.}, journal = {Nature Methods}, volume = {6}, year = {2009}, month = {2009 Apr}, pages = {297-303}, abstract = {

We introduce a method based on machine vision for automatically measuring aggression and courtship in Drosophila melanogaster. The genetic and neural circuit bases of these innate social behaviors are poorly understood. High-throughput behavioral screening in this genetically tractable model organism is a potentially powerful approach, but it is currently very laborious. Our system monitors interacting pairs of flies and computes their location, orientation and wing posture. These features are used for detecting behaviors exhibited during aggression and courtship. Among these, wing threat, lunging and tussling are specific to aggression; circling, wing extension (courtship {\textquoteright}song{\textquoteright}) and copulation are specific to courtship; locomotion and chasing are common to both. Ethograms may be constructed automatically from these measurements, saving considerable time and effort. This technology should enable large-scale screens for genes and neural circuits controlling courtship and aggression.

}, keywords = {Animal, Animals, Artificial Intelligence, Automated, Behavior, Computer-Assisted, Drosophila melanogaster, Humans, Image Interpretation, Monitoring, Movement, Pattern Recognition, Physiologic, Posture, Social Behavior}, issn = {1548-7105}, doi = {10.1038/nmeth.1310}, author = {Dankert, Heiko and Wang, Liming and Hoopfer, Eric D and Anderson, David J and Perona, Pietro} } @article {5758, title = {High-throughput ethomics in large groups of Drosophila.}, journal = {Nature Methods}, volume = {6}, year = {2009}, month = {2009 Jun}, pages = {451-7}, abstract = {

We present a camera-based method for automatically quantifying the individual and social behaviors of fruit flies, Drosophila melanogaster, interacting in a planar arena. Our system includes machine-vision algorithms that accurately track many individuals without swapping identities and classification algorithms that detect behaviors. The data may be represented as an ethogram that plots the time course of behaviors exhibited by each fly or as a vector that concisely captures the statistical properties of all behaviors displayed in a given period. We found that behavioral differences between individuals were consistent over time and were sufficient to accurately predict gender and genotype. In addition, we found that the relative positions of flies during social interactions vary according to gender, genotype and social environment. We expect that our software, which permits high-throughput screening, will complement existing molecular methods available in Drosophila, facilitating new investigations into the genetic and cellular basis of behavior.

}, keywords = {Animal, Animals, Artificial Intelligence, Automated, Behavior, Computer-Assisted, Drosophila melanogaster, Humans, Image Interpretation, Monitoring, Movement, Pattern Recognition, Physiologic, Posture, Social Behavior}, issn = {1548-7105}, doi = {10.1038/nmeth.1328}, author = {Branson, Kristin and Robie, Alice A and Bender, John and Perona, Pietro and Dickinson, Michael H} }