Scenario 4: End-to-End Research Workflows

High-level pipelines for complete research workflows—from data loading to analysis and visualization—without requiring detailed knowledge of model implementation.

Tutorials

Research Pipelines

Overview

This scenario demonstrates streamlined workflows for common research tasks using pre-built pipelines. Perfect for experimental neuroscientists and researchers who want to analyze data quickly without diving into implementation details.

Tutorial 1: Theta Sweep Pipeline

  • Complete theta sweep analysis in one line

  • Loading trajectory data from various sources

  • Automatic simulation and visualization

  • Customizable parameters for advanced users

  • Batch processing multiple datasets

Who Should Use Pipelines?

Perfect for:

  • Experimental neuroscientists without deep coding expertise

  • Rapid prototyping and exploratory analysis

  • Standardized processing of multiple datasets

  • Publication-quality figure generation

  • Teaching and demonstrations

Consider manual approach when:

  • Implementing non-standard model architectures

  • Developing new analysis methods

  • Need fine-grained control over every step

  • Extending pipeline functionality

Learning Path

Quick Start:

  1. Prepare your trajectory data (positions + timestamps)

  2. Run the pipeline with default parameters

  3. Examine generated plots and animations

  4. Customize parameters as needed

Advanced Usage:

  • Custom post-processing of simulation data

  • Batch processing multiple sessions

  • Parameter sweeps and optimization

  • Integration with existing analysis workflows

Prerequisites

  • Basic Python knowledge

  • Understanding of your experimental data format

  • Trajectory data (position over time)

Estimated Time

  • Tutorial 1: 30-35 minutes

  • Setting up for your own data: 15-30 minutes

  • Total: 60 minutes

Pipeline Features

The ThetaSweepPipeline provides:

  • Automatic data validation—Checks data format and quality

  • Network simulation—Direction cells and grid cells

  • Theta modulation—Speed-dependent oscillations

  • Visualization suite—Trajectory plots, population activity, animations

  • Raw data export—For custom analysis

  • Flexible configuration—From simple to advanced usage

Data Input Formats

Supported formats for trajectory data:

  • CSV files

  • NumPy arrays (.npy)

  • MATLAB files (.mat)

  • Pandas DataFrames

  • DeepLabCut output

  • Bonsai tracking output

  • Custom formats (with preprocessing)

Next Steps

After completing this scenario:

  • Apply pipelines to your own experimental data

  • Explore custom analysis using raw simulation outputs

  • Learn implementation details in Scenario 1 for customization

  • Contribute new pipelines to the library