2011
DOI: 10.1002/wcs.142
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Predictive coding

Abstract: Predictive coding is a unifying framework for understanding redundancy reduction and efficient coding in the nervous system. By transmitting only the unpredicted portions of an incoming sensory signal, predictive coding allows the nervous system to reduce redundancy and make full use of the limited dynamic range of neurons. Starting with the hypothesis of efficient coding as a design principle in the sensory system, predictive coding provides a functional explanation for a range of neural responses and many as… Show more

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Cited by 400 publications
(351 citation statements)
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References 74 publications
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“…Computational explanations of the balance between sensory evidence and prior knowledge as highlighted by our study are typically cast in terms of Bayesian models of perception and formalized within a predictive coding framework (5,31,32). In this setting, the integration of bottom-up and top-down signals is mediated by the relative amount of information each of these components provides: the stronger the sensory evidence is relative to prior knowledge, the more it will impact on the final processing output; conversely, if prior knowledge provides a relatively greater amount of information it will be weighted more strongly.…”
Section: Discussionmentioning
confidence: 99%
“…Computational explanations of the balance between sensory evidence and prior knowledge as highlighted by our study are typically cast in terms of Bayesian models of perception and formalized within a predictive coding framework (5,31,32). In this setting, the integration of bottom-up and top-down signals is mediated by the relative amount of information each of these components provides: the stronger the sensory evidence is relative to prior knowledge, the more it will impact on the final processing output; conversely, if prior knowledge provides a relatively greater amount of information it will be weighted more strongly.…”
Section: Discussionmentioning
confidence: 99%
“…The predictive coding framework proposes that information residing in an internal predictive model is fed back from higher‐order cortical areas to lower‐level brain areas whose activity reflects the difference between auditory input and the predictive information, that is, the prediction error signal (Friston, 2005; Huang & Rao, 2011; Mumford, 1992; Rao & Ballard, 1999). This error signal is projected to the higher‐order cortical areas through feedforward connections to update the internal model.…”
Section: Discussionmentioning
confidence: 99%
“…We note that most formulations of PC rely on Bayesian principles 14,58 , an approach that we have not employed in this study. There are multiple formulations of PC that use different Bayesian-based approaches to model surprise signals and perceptual learning 11 .…”
Section: Methodsmentioning
confidence: 99%
“…However, no study has examined whether faces become familiar and recognized in a manner that conforms to the assumptions of a PC account. Here we developed a computational model of face recognition around the assumptions of PC 11,14,15 , to examine how contextual and viewpoint independent information influence face recognition and its neural underpinnings.…”
mentioning
confidence: 99%