CageLab is a collaborative project to build a high-throughput and large-scale cognitive training and testing platform for many subjects. In-cage testing and training is a strong 3Rs refinement for cognitive neuroscience research. The problem with existing cognitive testing / training kiosks is they do not scale well as the subject count increases. We solve this by implementing a robust remote control interface for one-to-many or many-to-many interfaces between control and experiment systems, and architecting a data pipeline using a neuroscience database and task metadata specifications.
- 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.
Browse our repositories to see what we're working on.
Made with ❤️ by the CageLab team
