Autopilot

Autopilot coordinates execution of behavioral neuroscience experiments across networked heterogeneous hardware to provide distributed task control and standardized data and metadata capture.

Key Features:

• Distributed Experimentation: Leverages networked swarms of Raspberry Pis to distribute experimental tasks and control across multiple devices.
• Experimental Flexibility: Supports arbitrary combinations of hardware components and experimental designs within the same Python framework.
• Data Provenance: Captures data and task design parameters in a human-readable metadata format at the time of collection to support reproducibility and publication.
• Performance Efficiency: Provides high-level programming tools while delivering submillisecond performance and operation at a fraction of the cost of traditional setups.
• Plugin System: Includes a permissive plugin system for sharing and extending experiment code and device integrations.
• Scalable Architecture: Employs a flexible, scalable architecture to accommodate growth in device number and experimental complexity.

Scientific Applications:

• Coordinated behavioral experiments: Enables experiments that require precise timing and coordination across multiple devices and data streams in behavioral neuroscience.
• High-throughput studies: Supports scaling of experimental throughput through distributed device control and parallelization.
• Investigation of neural mechanisms: Facilitates reproducible collection of behavioral and metadata necessary for studies probing neural mechanisms underlying behavior.

Methodology:

Implements an open-source Python framework that orchestrates networked Raspberry Pis to distribute experimental tasks and records human-readable data and task design metadata at the time of collection.