Abstract
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.
Keywords
- Probabilistic programming
- Predictive coding
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Pfeffer, A., Lynn, S.K. (2019). Scruff: A Deep Probabilistic Cognitive Architecture for Predictive Processing. In: Samsonovich, A. (eds) Biologically Inspired Cognitive Architectures 2018. BICA 2018. Advances in Intelligent Systems and Computing, vol 848. Springer, Cham. https://doi.org/10.1007/978-3-319-99316-4_33
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DOI: https://doi.org/10.1007/978-3-319-99316-4_33
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