On the computational architecture of the neocortex

By David Mumford. 2004

845 citations recorded by the source corpus.

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In Cognitive Neuroscience

Citations in the corpus

Theory of Mind: A Neural Prediction Problem (2013, positive): “In fact, predictive coding was initially suggested as a model for visual perception (Barlow, 1961; Gregory, 1980; Mumford, 1992), using a visual error code that preferentially encodes unexpected visual information.”
Varieties of cognitive penetration in visual perception. (2014, neutral): “…coding has been put forward by Friston (2010) and Clark (2013) while the basic ideas have been developed already earlier going back to Helmholtz (1860), Sokolov (1960), Gegory (1968) and Mumford (1992) (see Clark (2013) and commentaries for an extensive review and the history of these ideas).”
A review of predictive coding algorithms. (2017, background): “1; Mumford, 1992), hypothesises that cortical feedback connections act to suppress information which is predicted by higher-level cortical regions.”
Action understanding and active inference (2011, positive): “The resulting scheme, when formulated in terms of prediction errors, corresponds to predictive coding (cf., Mumford 1992; Rao and Ballard 1998; Friston 2008).”
Interactive Activation and Mutual Constraint Satisfaction in Perception and Cognition (2014, background): “One very general open question is whether top-down and between-modality influences should be viewed as an additional sources of constraint on interpretation, as in the interactive activation framework, or whether, instead, top-down signals should be viewed as predictions that are compared with botto”
Canonical microcircuits for predictive coding. (2012, positive): “Furthermore, the resulting scheme corresponds almost exactly to the computational architecture proposed by Mumford (1992).”
Object perception as Bayesian inference. (2004, background): “…depth, or background clutter) may require an internal generative process that in some sense mirrors aspects of the external generative model used for inference (Grenander 1996, Mumford 1992,Rao & Ballard 1999,Tu & Zhu 2002a,Tu et al 2003) or for learning (Dayan et al 1995,Hinton & Ghahramani 1997).”
Functional and Effective Connectivity: A Review (2011, neutral): “Model comparison rests on the relative evidence for one model compared to another [see Penny et al. (2004) for a discussion in the context of functional magnetic resonance imaging (fMRI).”