You need to install pytorch, preferably with CUDA. In addition, install requirements.txt
$ pip install -r requirements.txt
$ python -m mikkel_sim
usage: __main__.py [-h] [-d N] [-s NUM_OF_STEPS] [-n NUM_NEURONS]
Create simulated neuron datasets
options:
-h, --help show this help message and exit
-d N, --num-data N Generate d datasets (default: 100)
-s NUM_OF_STEPS, --num-of-steps NUM_OF_STEPS
Number of time steps for the simulation (default: 100000)
-n NUM_NEURONS, --num-neurons NUM_NEURONS
Use n neurons in simulation (default: 20)
Make sure to specify the correct arguments in neuro_ml/__main__.py
, inside if __name__ == "__main__"
$ python -m neuro_ml
- X is very sparse so we could use a sparse representation for more efficient memory use
- Predict edge features instead of node features. This is more clear conceptually since the weights are associated with edges and is supported by edge_updater and edge_update in the MessagePassing base class
- Only use the inner MLP and make it more powerful (or use a transformer/lstm instead)
- Dependence on M (number of time steps that we calculate the co-firing rates for)