Scruff: A Deep Probabilistic Cognitive Architecture for Predictive Processing

The theory of predictive processing encompasses several elements that make it attractive as the underlying computational approach for a cognitive architecture. We introduce a new cognitive architecture, Scruff, capable of implementing predictive processing models by incorporating key properties of neural networks into the Bayesian probabilistic programming framework. We illustrate the Scruff approach with conditional linear Gaussian (CLG) models, noisy-or models, and a Bayesian variation of the Rao-Ballard linear algebra model of predictive vision.

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