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About 10,000 papers for “Predictive coding”

Predictive Coding

Stewart Shipp, Karl Friston · 2023

Abstract Predictive coding algorithms specify reciprocal message passing between hierarchical levels of a modular computation; likewise, the cerebral cortex can be characterized as a hierarchical arrangement of areas communicating via reciprocal forward and backward pathways. If the former is a model for the latter, it imposes several constraints over the micro- and macro-structure of pathways linking cortical areas, for example: which classes of neuron, with identified computational roles, make direct contact with each other; the topographic specificity of reciprocal connections, as realized through convergent and divergent connectivity; and the relative incidence of “manifold” processing (axonal distribution to multiple targets) in forward and backward connections. These issues are examined with reference to the visual system in mouse and monkey, with the conclusion that the mainstream organization of connectivity is largely compliant. Judicious expansion of the theoretical framework can accommodate some anomalous aspects of connectivity, and predict elements yet to be investigated experimentally.

Cited by 2Open access

Predictive Coding

Lajos Hanzo FREng, FIEEE, FIET, DSc, F. Clare Somerville M.Eng, PhD, Dr Jason Woodard BA, MSc, PhD · 2007

This chapter contains sections titled: Forward-Predictive Coding DPCM Codec Schematic Predictor Design Adaptive One-word-memory Quantisation DPCM Performance Backward-adaptive Prediction The 32 kbps G.721 ADPCM Codec Subjective and Objective Speech Quality Variable-rate G.726 and Embedded G.727 ADPCM Rate-distortion in Predictive Coding Chapter Summary

Cited by 0

Predictive coding

Yanping Huang, Rajesh P. N. Rao · Wiley Interdisciplinary Reviews Cognitive Science · 2011

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 aspects of brain organization. The lateral and temporal antagonism in receptive fields in the retina and lateral geniculate nucleus occur naturally as a consequence of predictive coding of natural images. In the higher visual system, predictive coding provides an explanation for oriented receptive fields and contextual effects as well as the hierarchical reciprocally connected organization of the cortex. Predictive coding has also been found to be consistent with a variety of neurophysiological and psychophysical data obtained from different areas of the brain. WIREs Cogni Sci 2011 2 580-593 DOI: 10.1002/wcs.142 For further resources related to this article, please visit the WIREs website.

Cited by 423

Predictive coding

haiqi Lin, Jinting Liu, Ke Ding · OSF Preprints (OSF Preprints) · 2024

Cited by 0

Predictive Coding

Ge Li, Wei Gao, Wen Gao · 2024

Prediction techniques have been widely used in video coding. Among those techniques, intra prediction is applied to remove spatial redundancy, while inter prediction is adopted to remove temporal redundancy. Point cloud compression based on the hybrid framework follows a similar design philosophy to video compression. As a result, intra and inter prediction are also adopted as essential methods to remove the information redundancy effectively.

Cited by 12Open access

Linear Predictive Coding

Muhammad Adil Raja · Bulletin of Miscellaneous Information (Royal Gardens Kew) · 2016

Cited by 5Open access

Bayesian Predictive Coding

Alexander Tschantz, Magnus Koudahl, Hampus Linander et al. · ArXiv.org · 2025

Predictive coding (PC) is an influential theory of information processing in the brain, providing a biologically plausible alternative to backpropagation. It is motivated in terms of Bayesian inference, as hidden states and parameters are optimised via gradient descent on variational free energy. However, implementations of PC rely on maximum \textit{a posteriori} (MAP) estimates of hidden states and maximum likelihood (ML) estimates of parameters, limiting their ability to quantify epistemic uncertainty. In this work, we investigate a Bayesian extension to PC that estimates a posterior distribution over network parameters. This approach, termed Bayesian Predictive coding (BPC), preserves the locality of PC and results in closed-form Hebbian weight updates. Compared to PC, our BPC algorithm converges in fewer epochs in the full-batch setting and remains competitive in the mini-batch setting. Additionally, we demonstrate that BPC offers uncertainty quantification comparable to existing methods in Bayesian deep learning, while also improving convergence properties. Together, these results suggest that BPC provides a biologically plausible method for Bayesian learning in the brain, as well as an attractive approach to uncertainty quantification in deep learning.

Cited by 0Open access

Linear Predictive Coding

A. El-Jaroudi · 2003

Abstract Most real‐world signals carry redundant information from one sample (or snapshot) to the next. The focus of this article is on the use of LP in redundancy removal or data compression for coding applications (also known as linear predictive coding (LPC)). The remainder of the article is organized as follows: LP is formulated and the optimal prediction parameters are derived; the computational aspects and algorithms for the implementation of LP are explored; examples and applications of LPC are presented, and finally, variations on LPC used for coding speech signals are discussed.

Cited by 3

Dynamic predictive coding.

Linxing Preston Jiang (17936170), Rajesh P. N. Rao (6725039) · Figshare · 2024

<p><b>(a)</b> Generative model for dynamic predictive coding. <b>(b)</b> Parameterization of the model. The higher-level state modulates the lower-level transition matrices through a top-down network (“hypernetwork”) . <b>(c)</b> A possible neural implementation of the generative model using cortical pyramidal neurons. Pyramidal neurons receive the top-down embedding vector input via synapses at apical dendrites and the current recurrent state vector via basal dendrites, and produce as their output the next state vector. <b>(d)</b> Schematic depiction of an inference step when the dynamics at the lower level is stable. The higher-level state remains stable due to minimal prediction errors. <b>(e)</b> Depiction of an inference step when the lower-level dynamics changes. The resulting large prediction errors drive updates to the higher-level state to account for the new lower-level dynamics.</p>

Cited by 0Open access

Lossless Predictive Coding

Majid Rabbani, P. Jones · SPIE eBooks · 2009

For typical images, the values of adjacent pixels are highly correlated; that is, a great deal of information about a pixel value can be obtained by inspecting its neighboring pixel values. This property is exploited in predictive coding techniques where an attempt is made to predict the value of a given pixel based on the values of the surrounding pixels. We first describe a general, but impractical, predictive coding scheme and then discuss modifications that yield a practical scheme.

Cited by 0

Bidirectional predictive coding

Gaspard Oliviers, Mufeng Tang, Rafał Bogacz · ArXiv.org · 2025

Predictive coding (PC) is an influential computational model of visual learning and inference in the brain. Classical PC was proposed as a top-down generative model, where the brain actively predicts upcoming visual inputs, and inference minimises the prediction errors. Recent studies have also shown that PC can be formulated as a discriminative model, where sensory inputs predict neural activities in a feedforward manner. However, experimental evidence suggests that the brain employs both generative and discriminative inference, while unidirectional PC models show degraded performance in tasks requiring bidirectional processing. In this work, we propose bidirectional PC (bPC), a PC model that incorporates both generative and discriminative inference while maintaining a biologically plausible circuit implementation. We show that bPC matches or outperforms unidirectional models in their specialised generative or discriminative tasks, by developing an energy landscape that simultaneously suits both tasks. We also demonstrate bPC's superior performance in two biologically relevant tasks including multimodal learning and inference with missing information, suggesting that bPC resembles biological visual inference more closely.

Cited by 1Open access

Complementing predictive coding

W. Brian Newsome · Frontiers in Psychology · 2012

GENERAL COMMENTARY article Front. Psychol., 18 December 2012Sec. Theoretical and Philosophical Psychology Volume 3 - 2012 | https://doi.org/10.3389/fpsyg.2012.00554

Cited by 2Open access

Predictive coding--II

Peter Eliaš · IEEE Transactions on Information Theory · 1955

In Part I predictive coding was defined and messages, prediction, entropy, and ideal coding were discussed. In the present paper the criterion to be used for predictors for the purpose of predictive coding is defined: that predictor is optimum in the information theory (IT) sense which minimizes the entropy of the average error-term distribution. Ordered averages of distributions are defined and it is shown that if a predictor gives an ordered average error term distribution it will be a best IT predictor. Special classes of messages are considered for which a best IT predictor can easily be found, and some examples are given. The error terms which are transmitted in predictive coding are treated as if they were statistically independent. If this is indeed the case, or a good approximation, then it is still necessary to show that sequences of message terms which are statistically independent may always be coded efficiently, without impractically large memory requirements, in order to show that predictive coding may be practical and efficient in such cases. This is done in the final section of this paper.

Cited by 92

Lossless predictive coding

J.W. McCoy, N. Magotra, S.D. Stearns · 2002

This paper describes the first stage of a two stage lossless data compression algorithm. The first stage consists of a lossless adaptive predictor. The term lossless implies that the original data can be recovered exactly. The second stage employs arithmetic coding. Results are presented for a seismic data base.

Cited by 27