Animal behavior

…genomics, synthetic biology, animal behavior, physiology and neural systems. Neural-systems research includes developmental, genetic, molecular and cellular organization, sensorimotor activation, synaptic and circuit plasticity, and computational approaches. The call also supports biological datasets and methods for observing, measuring, modeling and manipulating living systems. Single investigators, teams and consortia may propose; routine applications, product or

Traditional approaches to studying decision-making in neuroscience focus on simplified behavioral tasks where animals perform repetitive, stereotyped actions to receive explicit rewards. While informative, these methods constrain our understanding of decision-making to short timescale behaviors driven by explicit goals. In natural environments, animals exhibit more complex, long-term behaviors driven by intrinsic motivations that are often unobservable. Recent works in time-varying inverse reinforcement learning (IRL) aim to capture shifting motivations in long-term, freely moving behaviors. H

Understanding the neural basis of animal behavior is a central challenge in biology. In this work, we establish a connection between the spatio-temporal decision-making processes of animals and the ring attractor dynamics of neurons, which determine the organism's heading direction. We formulate a neural field model within the mean field approximation to provide a microscopic description of the spontaneous formation of bifurcations along their movement trajectories towards multiple targets, as observed in recent animal experiments. The direction of movement corresponds to the position of the a

Animal behavior results from the integration of sensory stimuli and internal models of the functioning of the environment. Adaptation hinges on optimizing actions for survival: maximizing rewards, and minimizing punishments. For this project, humans and rats were tested in non-sensory tasks, where informed gambling strategies enhance reward outcomes.Our two-alternative forced-choice foraging task involves rats initiating trials with a central nose-poke, followed by withdrawal and selection of a water reward spout based on the estimated reward likelihood on either side. Manipulating the probabi

Many neuroscience experiments involve manual scoring of animal behavior by a human operator. This operation is time consuming, is liable to bias and hinders reproducibility. For these reasons, automatic systems to score animal behavior from videos have gained increasing attention in recent years. Here, we present a machine learning approach to automatically score behavior in the Forced Swim Test (FST). During FST, animals perform three different behaviors: climbing, swimming and immobility. So far, these behaviors have been evaluated by an operator (a bias-prone method) or using automatic syst

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. DeepLabCut was the first tool to innovate in this space, and since the initial publication (Mathis et al. Nature Neuroscience 2018) has been downloaded more than half a million times. While the code-base was actively developed, after 6 years it is time for a major release. Here we present DeepLabCut 3.0, which builds on

…they enable the correlation of animal behavior with neuronal activity without limiting free movement. Combining this with the ability to use additional fluorescent markers in-vivo, would allow us to gather further information on the observed neurons, such as genetic traits, specific cell-types, or connections to other brain regions. Unfortunately, current miniscopes cannot discriminate more than one additional fluorophore, and sacrificing the animals for post-experiment brain fixation and ex

Fear is essential to an individual's survival. Abnormal fear will make people or animals more vulnerable to danger, and even lead to a series of neuropsychiatric diseases. Previous studies have suggested that functional abnormalities of the ventral tegmental area (VTA) may lead to fear-related neuropsychiatric diseases, such as post-traumatic stress disorder and schizophrenia, but the underlying mechanisms remain unclear. Our preliminary data has first shown that the projection from the central amygdala (CeA) to the ventral tegmental area could regulate fear. However, the specific cell types a

…to capture the rich neural and behavioral phenomenology associated with these regions, it remains difficult to compare these theories systematically against each other using the full range of empirical data. To address this gap, we built an open-source standardised software framework, named NeuralPlayground, to facilitate comparisons of hippocampus and entorhinal cortex models. This Python software package offers a repr

Quantification of animal behavior is critical to neuroscience, genetics, and ethology research. Despite the recent development of a deep-learning-based pose estimation model, the usage of those systems has been limited due to the various video recording environment and hardware systems. Here we introduce a novel open-field experimental hardware with multi-vision and a 3D real-time behavior analysis system, based on the efficient and rapid object-detection deep learning algorithm, named AVATAR (AI Vision Analysis for Three-dimensional Action in Real-time). Combining this system with an automate

A popular approach to quantifying animal behavior from video data is through behavioral segmentation, wherein video frames are labeled as containing one or more discrete behavior classes, such as grooming or rearing. These behaviors are often manually labeled, which is time consuming and error prone. An alternative approach is to train a sequence model which learns to map behavioral features extracted from video frames to discrete behaviors, although supervised models still require manually labeled examples to learn from. In order to reduce the need for expensive manual labels in this supervis

Quantitative methods that cluster videos of animal behavior into repeated syllables (sometimes called “motifs") have become fundamental tools for systems neuroscientists and neuroethologists. A popular approach is to use autoregressive hidden Markov models (ARHMMs) to identify behavioral syllables in the absence of labeled training data. This model, called MoSeq, has been shown to successfully segment depth-camera videos of mouse behavior into syllables that are familiar to a human observer. However, one issue with MoSeq is that it sorts behaviors which appear very similar to the human eye (e.