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About this paper
Arno Granier, Mihai A. Petrovici, Walter Senn, Katharina A. Wilmes
The authors develop a theory of cortical prediction in which higher-level areas estimate confidence, or inverse expected uncertainty, alongside predicted activity. Confidence weights ordinary prediction errors, while a second-order error compares predicted confidence with squared mismatch and supports learning confidence. In simulations, this lets a small network classify shared-mean classes that classical predictive coding cannot, not demonstrating cortical behavior.
Summary by alphaXiv.
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