Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.

By Rajesh P. N. Rao, Dana H. Ballard. 1999

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In Neuroscience

Citations in the corpus

Representation Learning with Contrastive Predictive Coding (2018, background): “In neuroscience, predictive coding theories suggest that the brain predicts observations at various levels of abstraction [7, 8].”
Theory of Mind: A Neural Prediction Problem (2013, neutral): “When the information that is being passed up from lower levels matches the information carried by the predictor neurons, the error neurons’ response to the input is reduced, ‘‘explaining away’’ the predictable input (Rao and Ballard, 1999).”
Counterfactuals uncover the modular structure of deep generative models (2018, background): “Availability of such structure allows an agent to efficiently manipulate and update internal representations in order to better interpret its sensory input, following a predictive coding principle [Rao and Ballard, 1999].”
Lifelong learning of human actions with deep neural network self-organization (2017, background): “This principle roughly resembles schemes of predictive coding (Huang & Rao, 2011; Rao & Ballard, 1999) proposing that the visual cortex can be modeled as a hierarchical network with reciprocal connections where top-down feedback connections from higher-order cortical areas convey predictions of…”
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning (2016, methodology): “As discussed in [39], although not explicit in their model, the separate error populations are analogous to the existence of on-center, off-surround and off-center, on-surround neurons early in the visual system.”
Recognition (2003, neutral): “AI has frequently drawn from neuroscience, including attention mechanisms (cognition) (Vaswani et al., 2017), predictive coding (Rao & Ballard, 1999), and hippocampal memory systems (Graves et al., 2016).”
Free-energy and the brain (2007, methodology): “…machine that conforms to the same principles that govern the interrogation of scientific data (MacKay, 1956; Neisser, 1967; Ballard et al, 1983; Mumford, 1992; Kawato et al 1993; Rao and Ballard 1998; Dayan et al, 1995; Friston, 2003; Körding and Wolpert 2004; Kersten et al 2004; Friston 2005).”
Free-Energy Minimization and the Dark-Room Problem (2012, background): “Perhaps the most popular incarnation of these schemes is predictive coding (Rao and Ballard, 1999).”