Multisensory Logic of Infant-Directed Aggression by Males.

Newborn mice emit signals that promote parenting from mothers and fathers but trigger aggressive responses from virgin males. Although pup-directed attacks by males require vomeronasal function, the specific infant cues that elicit this behavior are unknown. We developed a behavioral paradigm based on reconstituted pup cues and showed that discrete infant morphological features combined with salivary chemosignals elicit robust male aggression. Seven vomeronasal receptors were identified based on infant-mediated activity, and the involvement of two receptors, Vmn2r65 and Vmn2r88, in infant-directed aggression was demonstrated by genetic deletion. Using the activation of these receptors as readouts for biochemical fractionation, we isolated two pheromonal compounds, the submandibular gland protein C and hemoglobins. Unexpectedly, none of the identified vomeronasal receptors and associated cues were specific to pups. Thus, infant-mediated aggression by virgin males relies on the recognition of pup's physical traits in addition to parental and infant chemical cues.

Midbrain dopamine neurons signal phasic and ramping reward prediction error during goal-directed navigation.

Goal-directed navigation requires learning to accurately estimate location and select optimal actions in each location. Midbrain dopamine neurons are involved in reward value learning and have been linked to reward location learning. They are therefore ideally placed to provide teaching signals for goal-directed navigation. By imaging dopamine neural activity as mice learned to actively navigate a closed-loop virtual reality corridor to obtain reward, we observe phasic and pre-reward ramping dopamine activity, which are modulated by learning stage and task engagement. A Q-learning model incorporating position inference recapitulates our results, displaying prediction errors resembling phasic and ramping dopamine neural activity. The model predicts that ramping is followed by improved task performance, which we confirm in our experimental data, indicating that the dopamine ramp may have a teaching effect. Our results suggest that midbrain dopamine neurons encode phasic and ramping reward prediction error signals to improve goal-directed navigation.

Integrating hospital and community care: using a community virtual ward model to deliver combined specialist and generalist care to patients with severe chronic respiratory disease in their homes.

BACKGROUND: Chronic respiratory diseases are responsible for significant patient morbidity, mortality, and healthcare use. Community virtual ward (CVW) models of care have been successfully implemented to manage patients with complex medical conditions. AIMS: To explore the feasibility and clinical outcomes of a CVW model of care in patients with chronic respiratory disease. METHODS: Patients known to specialist respiratory services with Chronic Obstructive Pulmonary Disease (COPD) and/or asthma were admitted to the CVW for disease optimisation and exacerbation management. Individualised management plans were delivered in the patients' home by hospital-based respiratory and community nursing teams, incorporating remote technology to monitor vital signs. Symptoms and health status at admission and discharge were compared. RESULTS: Twenty patients were admitted. One-quarter of patients had asthma, 50% COPD, and 25% combined asthma/COPD. Patients had severe disease, mean (SD) FEV1 50(20) % predicted, and an average 6.4(5.7) exacerbations of disease in the previous 12 months. Patients received personalised disease and self-management education. All acute exacerbations (n = 11) were successfully treated in the community. The average length of CVW admission was 10(4) days. By discharge, 60% of COPD and 66% of asthma patients recorded improvements in symptoms score exceeding the minimal clinically important difference. Fifty percent had clinically meaningful improvements in health status. CONCLUSION: A CVW model facilitates the delivery of combined specialist and generalist care to patients with chronic respiratory disease in the community and improves symptoms and health status. The principles of the model are transferable to other conditions to improve overall health and reduce emergency hospital care.

Creating and controlling visual environments using BonVision.

Real-time rendering of closed-loop visual environments is important for next-generation understanding of brain function and behaviour, but is often prohibitively difficult for non-experts to implement and is limited to few laboratories worldwide. We developed BonVision as an easy-to-use open-source software for the display of virtual or augmented reality, as well as standard visual stimuli. BonVision has been tested on humans and mice, and is capable of supporting new experimental designs in other animal models of vision. As the architecture is based on the open-source Bonsai graphical programming language, BonVision benefits from native integration with experimental hardware. BonVision therefore enables easy implementation of closed-loop experiments, including real-time interaction with deep neural networks, and communication with behavioural and physiological measurement and manipulation devices.

Dopaminergic and Prefrontal Basis of Learning from Sensory Confidence and Reward Value.

Deciding between stimuli requires combining their learned value with one's sensory confidence. We trained mice in a visual task that probes this combination. Mouse choices reflected not only present confidence and past rewards but also past confidence. Their behavior conformed to a model that combines signal detection with reinforcement learning. In the model, the predicted value of the chosen option is the product of sensory confidence and learned value. We found precise correlates of this variable in the pre-outcome activity of midbrain dopamine neurons and of medial prefrontal cortical neurons. However, only the latter played a causal role: inactivating medial prefrontal cortex before outcome strengthened learning from the outcome. Dopamine neurons played a causal role only after outcome, when they encoded reward prediction errors graded by confidence, influencing subsequent choices. These results reveal neural signals that combine reward value with sensory confidence and guide subsequent learning.