pyControl

pyControl controls and sequences behavioral neuroscience experiments by defining tasks as Python-based extended state machines and interfacing with laboratory hardware for precise experimental control.

Key Features:

• Python-based extended state machines: Defines behavioral tasks using Python scripts structured as extended state machines to express complex task logic.
• Hardware modules: Includes hardware modules to construct diverse behavioral setups and implement task-specific hardware interactions.
• Scalability and high-throughput control: Supports scalable control of multiple experimental setups to enable high-throughput experimentation.
• Reproducibility and self-documenting task definitions: Employs highly readable, self-documenting task definitions to facilitate reproducibility and communication of experimental protocols.

Scientific Applications:

• Behavioral task implementation: Implementation and execution of richly structured behavioral tasks for neuroscience experiments.
• Linking behavior and neural mechanisms: Experiments probing interactions between behavior and neural activity under controlled conditions.
• Large-scale behavioral data collection: High-throughput experimental campaigns requiring standardized task definitions and scalable control across setups.

Methodology:

Experimental protocols are specified using Python scripts structured as extended state machines, and hardware modules are integrated to implement these protocols on laboratory setups.