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Predictive coding with spiking neural networks: A survey.

Antony W N'dri1, William Gebhardt2, Céline Teulière1

  • 1Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut Pascal, Clermont-Ferrand, F-63000, France.

Neural Networks : the Official Journal of the International Neural Network Society
|December 7, 2025
PubMed
Summary
This summary is machine-generated.

This review explores spiking predictive coding, a neuro-mimetic model using discrete neuron spikes. It categorizes how prediction errors are encoded, aiding brain-inspired computing advancements.

Keywords:
Brain-inspired computingEfficient codingNeuromorphic computingPredictive codingSpiking neural networks

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Neuromorphic Engineering

Background:

  • Predictive processing is a framework explaining brain function.
  • Neurons communicate using discrete action potentials (spikes).
  • Integrating predictive processing with spiking neurons is an active research area.

Purpose of the Study:

  • To review neuro-mimetic computational models of spiking predictive coding.
  • To organize these models based on prediction error representation.
  • To discuss applications and future directions in brain-inspired computing.

Main Methods:

  • Theoretical review of spiking predictive coding frameworks.
  • Categorization of models based on prediction error encoding mechanisms (error neurons, membrane potentials, implicit encoding).
  • Examination of energy-efficient hardware implementations for edge computing.

Main Results:

  • Three main classes of spiking predictive coding models identified based on prediction error representation.
  • Demonstrated applications in energy-efficient edge computing hardware.
  • Highlighted challenges and future research avenues in neuromorphic computing.

Conclusions:

  • Spiking predictive coding offers a promising framework for brain-inspired computing.
  • Understanding prediction error representation is key to developing efficient neuromorphic systems.
  • Further research is needed to advance neuromorphic formulations and implementations of predictive coding.