wormvae

We provide offical PyTorch implementations for connectome-constrained latent variable model of whole-brain neural activity.

The details of algorithm are presented in the paper Connectome-constrained Latent Variable Model of Whole-brain Neural Activity, ICLR 2022.

Prerequisites

• Linux or macOS
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN

Installation

• Clone this repo:

git clone https://github.com/TuragaLab/wormvae.git
cd wormvae

• Install dependencies by using conda env create -f environment.yml.
• Activate conda enviroment by using conda activate wormvae.

Datasets Visualization

Worm Connectivity

• Data folder data/worm_connectivity provides the connectome from C.elegans from the paper The Structure of the Nervous System of the Nematode Caenorhabditis elegans (The Mind of a Worm).
• C.elegan Connectome.
  

Worm Activity

• Data folder data/worm_activity provides the measured fluorescence trace from C.elegans given chemosensory stimulus from the paper NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in C. elegans.
• NeuroPAL Calcium Imaging of C.elegans.
  

Scripts Usage

Model Training

Call the following script in experiment/ to train a latent variable model for whole-brain neural activity.

python main.py --neuron_holdout list_of_neuron_holdout --train_worm list_of_worm_id --model_type model_type --constraint constraint --random_init_index random_init_index 

• --neuron_holdout defines the set of neurons to be held out during training. You can specify a list of neuron names to choose which neurons (typically a pair of symmetric neurons) to be held out, e.g. --neuron_holdout ['BAGL','BAGR'].
• --train_worm defines the set of worms to be trained. You can specify a list of worm ids to choose which worms to be trained on, e.g. --train_worm [1].
• --model_type defines the types of synapse modeling. You can use --model_type conductance to specify a conductance-based modeling, while --model_type current to specify a current-based modeling.
• --constraint defines the constraint to synapse weight. --constraint weight indicates using connectome synapse count to constrain the weight; --constraint sparsity indicates using connectome synapse sparsity (binarized matrix) to constrain the sparsity, while the magnitude is still trainable; --constraint unconstrained indicates without connectome constrained, the weight is fully-connected matrix and the magnitude is trainable.
• --random_init_index defines the index of random initialization in each training trial. In the paper, we use 4 different random initializations to evaluate the models, e.g. --random_init_index 0.

Results

Checkpints are saved as experiment/chechpoints/task_name.pt files , the training logging are saved as experiment/logs/task_name.log file; and the training loss trajectory are saved in the experiment/loss_trajectories/task_name.pickle. task_name is automatically defined with the options of arguments (neuron holdout, train worm, model types, constraint, etc.) described above.

Sample Results

• Neuron holdout under different type of synapse model types and levels of connectome constraints.
  
• Measured fluorescence traces (including unrecorded neurons) and inferred voltage for whole-brain (300 neurons).