Predictive Processing: A Canonical Cortical Computation

By Georg B. Keller, Thomas D. Mrsic-Flogel. Neuron, 2018

676 citations recorded by the source corpus.

This preview describes predictive processing as a computational framework for understanding cortical function in the context of emerging evidence with a focus on sensory processing. We discuss how the predictive processing framework may be implemented at the level of cortical circuits and how its implementation could be falsified experimentally. Lastly, we summarize the general implications of predictive processing on cortical function in healthy and diseased states.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/101c6ed6493f999c15604f8234622fd8e4d4d62e

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Causes and consequences of representational drift (2019, background): “Much work has highlighted the concept of predictive coding and error signals in the context of comparisons between internal predictions and incoming sensory signals [33 ,46].”
Brains and algorithms partially converge in natural language processing (2022, background): “Speci ﬁ - cally, while there is growing evidence that the human brain does predict words from context 30,73,74 , this learning rule may not fully account for the complex (and potentially various) tasks performed by the brain (e.g., long-range 75,76 and hierarchical predictions 77 ).”
ROOTS: Object-Centric Representation and Rendering of 3D Scenes (2020, background): “y understanding a scene is believed to be crucial in enabling various advanced cognitive functions in human-like AI systems [32] such as reasoning [3], causal inference [42, 38], imagining the future [27, 24, 41], and out-of-distribution generalization [1, 38]. A particularly relevant work in learni”
Sequential and efficient neural-population coding of complex task information (2019, neutral): “Hypothesized neocortical functions such as predictive coding (Bastos et al., 2012; Keller and Mrsic-Flogel, 2018; Rao and Ballard, 1999) and Bayesian inference (von Helmholtz, 1867; Ma et al.”
Conscious Processing and the Global Neuronal Workspace Hypothesis (2020, background): “10 (Keller and Mrsic-Flogel, 2018; Kersten et al., 2004; Rao and Ballard, 1999), leading to a more precise functional interpretation of top-down broadcasting.”