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Related Experiment Video

Updated: Jan 9, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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Published on: March 25, 2014

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

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 computing Efficient coding Neuromorphic computing Predictive coding Spiking neural networks

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Last Updated: Jan 9, 2026

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Published on: March 25, 2014

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Published on: November 12, 2019

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.