NERV: A Comprehensive Framework for Rapid, Reproducible, and Hardware-Synchronized Neuroscience Experiment Design and Execution | JoVE Visualize

Area of Science:

Neuroscience
Computational Neuroscience
Behavioral Science

Background:

Behavioral neuroscience experiments demand precise stimulus control, millisecond timing, hardware integration, and reliable data provenance.
The increasing complexity of 3D environments and multimodal recordings in neuroscience research presents challenges for tool development, accessibility, and reproducibility.
Current fragmented tools often separate stimulus presentation, synchronization, and data logging, leading to significant inefficiencies in experimental workflows.

Purpose of the Study:

To introduce the Neuroscience Experimental Runtime by Vanderbilt (NERV), a novel Unity-based framework designed to unify experiment design, execution, and data logging.
To enable rapid, no-code prototyping for neuroscience experiments through automated scene and script generation, precise event timing, and state management.
To provide a modular, open-source solution that lowers the barrier to entry for non-programmers while remaining extensible for advanced customization in neuroscience research.

Main Methods:

NERV utilizes the Unity game engine to create a unified experimental environment.
The framework automates scene and script generation, event timing, state management, and hardware-synchronized data acquisition.
It ensures robust data provenance by archiving code and experimental configurations alongside recorded data.

Main Results:

NERV demonstrated stable millisecond precision, with Unity-to-TTL delay averaging 2.10 ± 1.21 ms.
Frame-locked timing was confirmed, with TTL-to-photodiode delay at 28.93 ± 0.76 ms and Unity-to-screen delay at 31.04 ± 1.41 ms across 500 trials.
These timing accuracies enable reliable alignment of neural, behavioral, and visual event data in neuroscience experiments.

Conclusions:

NERV successfully combines millisecond precision, modular open-source design, and comprehensive data provenance into a single, integrated platform.
The framework addresses limitations of existing tools by reducing workflow fragmentation and enhancing the reproducibility and scalability of neuroscience research.
NERV provides an accessible yet extensible solution that accelerates development and establishes a robust foundation for next-generation neuroscience studies.