Open-source, Python-based, hardware and software for controlling behavioural neuroscience experiments

Laboratory behavioural tasks are an essential research tool. As questions asked of behaviour and brain activity become more sophisticated, the ability to specify and run richly structured tasks becomes more important. An increasing focus on reproducibility also necessitates accurate communication of task logic to other researchers. To these ends, we developed pyControl, a system of open-source hardware and software for controlling behavioural experiments comprising a simple yet flexible Python-based syntax for specifying tasks as extended state machines, hardware modules for building behavioural setups, and a graphical user interface designed for efficiently running high-throughput experiments on many setups in parallel, all with extensive online documentation. These tools make it quicker, easier, and cheaper to implement rich behavioural tasks at scale. As important, pyControl facilitates communication and reproducibility of behavioural experiments through a highly readable task definition syntax and self-documenting features. Here, we outline the system’s design and rationale, present validation experiments characterising system performance, and demonstrate example applications in freely moving and head-fixed mouse behaviour

Multimodal cues displayed by submissive rats promote prosocial choices by dominants

Animals often display prosocial behaviors, performing actions that benefit others. Although prosociality is essential for social bonding and cooperation, we still know little about how animals integrate behavioral cues from those in need to make decisions that increase their well-being. To address this question, we used a two-choice task where rats can provide rewards to a conspecific in the absence of self-benefit and investigated which conditions promote prosociality by manipulating the social context of the interacting animals. Although sex or degree of familiarity did not affect prosocial choices in rats, social hierarchy revealed to be a potent modulator, with dominant decision-makers showing faster emergence and higher levels of prosocial choices toward their submissive cage mates. Leveraging quantitative analysis of multimodal social dynamics prior to choice, we identified that pairs with dominant decision-makers exhibited more proximal interactions. Interestingly, these closer interactions were driven by submissive animals that modulated their position and movement following their dominants and whose 50-kHz vocalization rate correlated with dominants’ prosociality. Moreover, Granger causality revealed stronger bidirectional influences in pairs with dominant focals and submissive recipients, indicating increased behavioral coordination. Finally, multivariate analysis highlighted body language as the main information dominants use on a trial-by-trial basis to learn that their actions have effects on others. Our results provide a refined understanding of the behavioral dynamics that rats use for action-selection upon perception of socially relevant cues and navigate social decision-making.This work was supported by the NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation (grant 26478 to C.M.), Fundaçao Bial (250/16 to C.M.), and the Spanish Agency of Research (grant RTI2018-097843-B-100 to C.M.). M.J.M.G. was supported by “la Caixa” Foundation (ID 100010434), under the agreement LCF/BQ/DI17/11620002, and by Fondazione Ing. Aldo Gini. D.A.L. received a travel grant from the Carolina Foundation in the context of this collaboration.Peer reviewe