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A review of predictive coding algorithms.

M W Spratling¹

¹King's College London, Department of Informatics, London, UK.

Brain and Cognition

|January 27, 2016

Summary

This summary is machine-generated.

This review clarifies distinct predictive coding algorithms used in brain research. It highlights how these models, despite aiming to fit sensory data, vary in generative models, optimization, and neurobiological links.

Keywords:

Cortex Free energy Neural networks Predictive coding Retina Signal processing

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Area of Science:

Neuroscience
Computational Neuroscience
Signal Processing

Background:

Predictive coding is a prominent theory explaining brain's probabilistic inference.
The term 'predictive coding' encompasses several distinct algorithms.
Understanding these variations is crucial for advancing computational neuroscience.

Purpose of the Study:

To provide a concise review of different predictive coding algorithms.
To highlight the similarities and differences among these algorithms.
To clarify the application of predictive coding in neuroscience and signal processing.

Main Methods:

Review of five distinct predictive coding algorithms.
Comparison of algorithms based on generative model type, optimization process, and neurobiological relevance.
Analysis of historical and modern applications in signal processing and neuroscience.

Main Results:

Five algorithms are detailed: linear predictive coding, retinal predictive coding, and three cortical models.
Algorithms share the goal of fitting a generative model to sensory data.
Key differences lie in the generative model, optimization strategy, and neurobiological grounding.

Conclusions:

Predictive coding algorithms, while unified in principle, exhibit significant diversity.
This diversity impacts their application and interpretation in understanding brain function.
Further research can clarify the specific roles of different predictive coding variants in neural processing.