Physical features of sensory stimuli are fixed, but sensory perception is context-dependent. The precise mechanisms that govern contextual modulation remain unknown. Here, we trained mice to switch between two contexts: passively listening to pure tones vs. performing a recognition task for the same stimuli. Two-photon imaging showed that many excitatory neurons in auditory cortex were suppressed, while some cells became more active during behavior. Whole-cell recordings showed that excitatory inputs were only modestly affected by context, but inhibition was more sensitive, with PV, SOM+, and VIP+ interneurons balancing inhibition/disinhibition within the network. Cholinergic modulation was involved in context-switching, with cholinergic axons increasing activity during behavior and directly depolarizing inhibitory cells. Network modeling captured these findings, but only when modulation coincidently drove all three interneuron subtypes, ruling out either inhibition or disinhibition alone as sole mechanism for active engagement. Parallel processing of cholinergic modulation by cortical interneurons therefore enables context-dependent behavior.

The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior. While advances in deep learning and computer vision have enabled markerless pose estimation in individual animals, extending these to multiple animals presents unique challenges for studies of social behaviors or animals in their natural environments. Here we present Social LEAP Estimates Animal Poses (SLEAP), a machine learning system for multi-animal pose tracking. This system enables versatile workflows for data labeling, model training and inference on previously unseen data. SLEAP features an accessible graphical user interface, a standardized data model, a reproducible configuration system, over 30 model architectures, two approaches to part grouping and two approaches to identity tracking. We applied SLEAP to seven datasets across flies, bees, mice and gerbils to systematically evaluate each approach and architecture, and we compare it with other existing approaches. SLEAP achieves greater accuracy and speeds of more than 800 frames per second, with latencies of less than 3.5 ms at full 1,024 × 1,024 image resolution. This makes SLEAP usable for real-time applications, which we demonstrate by controlling the behavior of one animal on the basis of the tracking and detection of social interactions with another animal.

The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation to quantify and model natural animal behavior. This has led to important advances in deep learning-based markerless pose estimation that have been enabled in part by the success of deep learning for computer vision applications. Here we present SLEAP (Social LEAP Estimates Animal Poses), a framework for multi-animal pose tracking via deep learning. This system is capable of simultaneously tracking any number of animals during social interactions and across a variety of experimental conditions. SLEAP implements several complementary approaches for dealing with the problems inherent in moving from single-to multi-animal pose tracking, including configurable neural network architectures, inference techniques, and tracking algorithms, enabling easy specialization and tuning for particular experimental conditions or performance requirements. We report results on multiple datasets of socially interacting animals (flies, bees, and mice) and describe how dataset-specific properties can be leveraged to determine the best configuration of SLEAP models. Using a high accuracy model (<2.8 px error on 95% of points), we were able to track two animals from full size 1024 × 1024 pixel frames at up to 320 FPS. The SLEAP framework comes with a sophisticated graphical user interface, multi-platform support, Colab-based GPU-free training and inference, and complete tutorials available, in addition to the datasets, at sleap.ai.

Vagus nerve stimulation (VNS) suppresses inflammation and autoimmune diseases in preclinical and clinical studies. The underlying molecular, neurological, and anatomical mechanisms have been well characterized using acute electrophysiological stimulation of the vagus. However, there are several unanswered mechanistic questions about the effects of chronic VNS, which require solving numerous technical challenges for a long-term interface with the vagus in mice. Here, we describe a scalable model for long-term VNS in mice developed and validated in 4 research laboratories. We observed significant heart rate responses for at least 4 weeks in 60-90% of animals. Device implantation did not impair vagus-mediated reflexes. VNS using this implant significantly suppressed TNF levels in endotoxemia. Histological examination of implanted nerves revealed fibrotic encapsulation without axonal pathology. This model may be useful to study the physiology of the vagus and provides a tool to systematically investigate long-term VNS as therapy for chronic diseases modeled in mice.