Predictive Coding in the Visual Cortex

Rao, R. P. N., & Ballard, D. H. (1999). Predictive Coding in the Visual Cortex: A Functional Interpretation of Some Extra-Classical Receptive-Field Effects. Nature Neuroscience, 2(1), 79-87.

Type

Journal article

Intellectual base

Primary research

Year

1999

DOI

10.1038/4580

Link

https://doi.org/10.1038/4580

Cited from

engineering_design, neuroscience, psychology, statistics_experimental_design

Cited by

4 citations across 4 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Feedforward
  • Not predictive coding. `predictive_coding` is a particular neuroscientific theory in which the brain propagates prediction errors up a hierarchy; feedforward is the substrate-neutral pattern of placing a consequence-model upstream of action, of which predictive coding's forward model is one biological instance.

    This sourceThe predictive-coding theory in which the cortex propagates prediction errors up a hierarchy; its forward model is one biological instance of upstream consequence-modeling, supporting the contrast with predictive coding.

• Object Permanence
  • State-space models from econometrics inform cognitive-neuroscience accounts of perceptual inference, where hidden-state inference and predictive coding are object permanence for perceptual causes.

    This sourceCasts perception as hidden-state inference with prediction of latent causes, linking state-space/predictive-coding accounts to object permanence for perceptual causes.

• Prediction Error
  • Forecasting and statistics: forecast residuals expose bias, autocorrelation, changing variance, and misspecified dynamics. Control and filtering: innovations update state estimates and determine corrective gain. Signal coding: a shared predictor allows systems to transmit the residual rather than the full expected signal. Neuroscience: hierarchical models compare predicted with incoming activity and propagate mismatch.
• Predictive Coding
  • The essential commitment is that the expected part of the signal is suppressed and only the surprising part propagates; the model is then updated by the error so that future predictions improve.

    This sourceSeminal computational-neuroscience model of the cortex as a hierarchy of predictors in which higher areas send predictions downward and lower areas return only the residual error upward; grounds the core predict–compare–correct loop and its structural separation of predicted from error component.

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