IBL Electrophysiology Feature Computation and Region Inference

This repository contains tools for computing electrophysiology features and performing region inference from neural recordings.

Installation

1. First, activate the IBL conda environment:

conda activate iblenv

2. Clone the ibleatools repository, and cd into it

cd ...yourpath/ibleatools

3. Once in the ibleatools folder, install the required packages using pip:

pip install -e .

Usage

The main interface is through main.py, which can be run using a configuration file:

python main.py --config config.yaml

Configuration File

The configuration is managed through a YAML file. To avoid committing local changes, the actual configuration file (config.yaml) is ignored by git. Instead, a template file (config_template.yaml) is provided. To use the tool:

1. Copy the template file to create your local configuration:

cp config_template.yaml config.yaml

2. Edit config.yaml with your specific settings:

# Required parameters
pid: "5246af08-0730-40f7-83de-29b5d62b9b6d"  # Probe ID
t_start: 300.0  # Start time in seconds
duration: 3.0  # Duration in seconds

# Operation mode
mode: "both"  # Options: 'features', 'inference', or 'both'

# Optional parameters
features_path: "/path/to/output_dir"  # Path to output directory.
model_path: "/path/to/model"  # Path to the model directory for region inference

Configuration Parameters

• Required Parameters:

  • pid: Probe ID for the recording
  • t_start: Start time in seconds
  • duration: Duration of the analysis in seconds

• Operation Mode:

  • mode: Specifies which operations to perform
    • features: Only compute features
    • inference: Only perform region inference
    • both: Perform both feature computation and region inference

• Optional Parameters:

  • output_dir: Path to output directory
  • model_path: Path to the model directory for region inference. If not provided, a default path will be used

Features

The tool performs two main operations:

1. Feature Computation:

   • Computes various electrophysiology features from the raw data
   • Saves features in Parquet format for efficient storage and retrieval

2. Region Inference:

   • Uses pre-trained models to infer brain regions
   • Can be run independently if features are already computed

Output

• Features are saved in Parquet format for efficient storage
• Region inference results include predicted regions and their probabilities
• All operations are logged for tracking and debugging