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Towards an International Brain Laboratory (IBL) Foundation Model

We provide a codebase for Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution. Neuroscience research has made immense progress over the last decade, but our understanding of the brain remains fragmented and piecemeal: the dream of probing an arbitrary brain region and automatically reading out the information encoded in its neural activity remains out of reach. In this work, we build towards a first foundation model for neural spiking data that can solve a diverse set of tasks across multiple brain areas.

Environment setup

Create conda environment

conda env create -f env.yaml

Activate the environment

conda activate ibl-fm

Datasets and Models

Hugging Face

We have uploaded both the processed sessions from the IBL dataset and the pre-trained multi-session models to Hugging Face. You can access these resources here. Datasets are only visible to members of the organization, so please click to join the org to access the datasets. Downloading them is straightforward and allows you to integrate these models and datasets seamlessly into your projects.

Training Multi/Single Session Models (SSL)

Setup and Start Training

  1. Navigate to the script directory:

    cd script

  2. Start the training process:

    source train_sessions.sh

Configuration Adjustments

  • Modify Model Configurations: To change the model for training, update the YAML files in src/configs and adjust settings in src/train_sessions.py.

  • Example of Trainer and Mode Configurations:

    # Default setting
# Load configuration
kwargs = {
  "model": "include:src/configs/ndt1_stitching.yaml"
}
config = config_from_kwargs(kwargs)
config = update_config("src/configs/ndt1_stitching.yaml", config)
config = update_config("src/configs/ssl_sessions_trainer.yaml", config)

  • Setting the Number of Sessions: To determine the number of sessions for training, edit ssl_sessions_trainer.yaml. The paper used configurations of 1, 10, or 34 sessions.

    num_sessions: 10  # Number of sessions to use in SSL training.

  • Training Logs: Training logs will be uploaded to Weights & Biases (wandb) and saved in the results folder.

Fine-Tuning and Evaluating the Pre-trained Model

Notes

The scripts provided are designed for use on a High-Performance Computing (HPC) environment with Slurm. They allow for fine-tuning and evaluation of the model using multiple test sessions.

Running Multi-Session Fine-Tuning and Evaluation

  1. Script for Multiple Sessions: To submit jobs for all test sessions listed in data/test_re_eids.txt for fine-tuning and evaluation, use the following command: 
    source run_finetune_multi_session.sh NDT1 all 10 train-eval

Running Single Test Session Fine-Tuning and Evaluation

  1. Script for a Single Session: To execute fine-tuning and evaluation for a specific test session, use the command below. Replace the placeholder for EID with the actual unique ID of the test session. 
    source finetune_eval_multi_session.sh NDT1 all 10 5dcee0eb-b34d-4652-acc3-d10afc6eae68 train-eval

Parameters Explanation

  • MODEL_NAME: The name of the model (e.g., NDT1, NDT2).
  • MASK_MODE: The masking mode to apply (e.g., all, temporal).
  • NUM_TRAIN_SESSIONS: Number of training sessions to be used (e.g., 1, 10, 34).
  • EID: Unique identifier for a specific test session.
  • MODE: The operation mode (e.g., train, eval, train-eval).

Output

Both scripts load the pre-trained model from the results folder and save the evaluation results in .npy files.

Reading Out Results

Visualizing Results

  1. Navigate to the script directory:

    cd script

  2. Run the visualization script:

    source draw.sh NUM_TRAIN_SESSIONS

    This script outputs images visualizing results metrics, which are stored in the results/table folder.

Models

Neural Data Transformer (NDT1) - re-implementation

The configuration for NDT1 is src/configs/ndt1.yaml. Set the number of neurons by:


n_channels: NUM_NEURON # number of neurons recorded