A software platform for real-time and adaptive neuroscience experiments

Area of Science:

• Neuroscience
• Computational Biology
• Bioengineering

Background:

• Traditional neuroscience research often relies on pre-set hypotheses and retrospective data analysis.
• Adaptive experimental designs, driven by computational modeling, promise more efficient scientific discovery.
• Real-time integration of software and hardware is crucial for adaptive experimental paradigms.

Purpose of the Study:

• To introduce 'improv', a novel software platform for adaptive experimental designs.
• To demonstrate the platform's capability in integrating modeling, data collection, analysis, and live experimental control.
• To showcase the utility of improv for discovery and validation in neuroscience.

Main Methods:

• Developed 'improv', a flexible software platform.
• Integrated computational modeling with real-time data acquisition and analysis.
• Orchestrated live experiments including behavioral analysis, calcium imaging, stimulus selection, and optogenetic control.

Main Results:

• Demonstrated the efficacy of improv in both in silico and in vivo experiments.
• Successfully conducted real-time behavioral analyses and functional typing of neural responses.
• Enabled model-driven optogenetic manipulation of neurons in zebrafish.

Conclusions:

• Improv effectively integrates modeling, data collection, and experimental control for adaptive neuroscience research.
• The platform supports diverse model organisms and data types, enhancing experimental efficiency.
• Improv paves the way for next-generation adaptive experiments in neuroscience.