EPI brings recent machine learning techniques – the use of deep generative models for probabilistic inference – to bear on the problem of learning distributions of parameters that produce the specified properties of computation in theoretical models. Importantly, the techniques introduced offer a principled means to understand the implications of model parameter choices on computational properties of interest. This implementation of EPI performs hyperparameter optimization for a 2D LDS model constrained to produce a periodic signal of given frequency. Used to demo NeuroCAAS and EPI at COSYNE 2020.
This analysis trains an EPI model to produce a periodic signal, starting at different hyperparameter values.
Args:
-Input: (json file) A JSON file formatted with a single value: the random seed used to initialize hyperparameters, like so:
{
"random_seed": INT
}
-Config: (yaml file) A YAML file specifying NeuroCAAS parameters for the job duration expected. No analysis specific parameters required. Click on the config provided for an example.
Returns:
-Folder of results providing trained model, optimization history, and diagnostic output. See "epi_opt.mp4" for a video of learned parameter distributions.
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