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.
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