Abstract
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.
Highlights
Animal behaviour is of fundamental scientific interest, both in its own right and in relation to brain function (Krakauer et al, 2017)
System overview pyControl consists of three components, the pyControl framework, hardware, and graphical user interface (GUI)
Discussion pyControl is an open-source system for running behavioural experiments, whose principal strengths are (1) a flexible and intuitive Python-b ased syntax for programming tasks; (2) inexpensive, simple, and extensible behavioural hardware that can be purchased commercially or assembled by the user; (3) a GUI designed for efficiently running high-throughput experiments on many setups in parallel from a single computer; and (4) extensive online documentation and user support
Summary
Animal behaviour is of fundamental scientific interest, both in its own right and in relation to brain function (Krakauer et al, 2017). Though understanding natural behaviour is the ultimate goal, the tight control offered by laboratory tasks remains an essential tool in characterising learning mechanisms. To serve the needs of contemporary neuroscience, hardware and software for controlling behavioural experiments should be both flexible and easy to use. An increasing focus on reproducibility (Baker, 2016; International Brain Laboratory et al, 2021) necessitates that behaviour control systems facilitate communication and replication of behavioural paradigms across labs. Proprietary closed-s ource hardware and software make it difficult to extend or adapt functionality beyond explicitly implemented use cases. Programming behavioural tasks on commercial systems can be surprisingly non- user-friendly, perhaps due to limitations of underlying legacy hardware. Commercial hardware is typically very expensive considering the level of technology it represents, disadvantaging researchers outside well-funded institutions (Marder, 2013; Maia Chagas, 2018), and constraining the ability to scale behavioural assays for high throughput
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