CageLab is a collaborative project to build a high-throughput and large-scale cognitive training and testing device for many subjects. In-cage testing and training is a 3Rs refinement for cognitive neuroscience research. The problem with existing cognitive testing / training systems is they do not scale well as the subject count increases.

• Hardware - build a low-cost and flexible to adjust cage-attached box, along with reward and input devices. Low-cost is important because as the number of devices increases, rprice-per-device becomes an issue. Using Aluminium T-bar allows quick adaptation to different housing configurations compared to perspex of stainless steel enclosures.
• Middleware (cogmoteGO) - a fast and flexible way to distribute neuroscience experiments and collect data from many devices. It uses a HTTP API and talks via ØMQ messaging to experimental code for robust many-to-many control.
• Software - PsychToolbox-based experimental control, enabling existing experiment code designed for the lab to work more quickly in the home environment. PTB, with the largest support of different device hardware and best-in-class timing remains the gold-standard way to run neuroscience tasks.
• Data pipeline - integrating Alyx (International Brain Lab ONE protocol pipeline) to scale data collection to a large number of home environment test devices.
• Task Design - Automated cognitive training using a tuned asymmetric staircase: more standardised and adaptive training per subject, hopefully resulting is faster training times.

Interested in contributing or learning more? Check out our repositories and feel free to explore, fork, and contribute!

Browse our repositories to see what we're working on.

Made with ❤️ by the CageLab team