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Predictive Coding Light

Author

Listed:
  • Antony W. N’dri

    (Institut Pascal)

  • Thomas Barbier

    (Institut Pascal)

  • Céline Teulière

    (Institut Pascal)

  • Jochen Triesch

    (Institut Pascal
    Frankfurt Institute for Advanced Studies)

Abstract

Current machine learning systems consume vastly more energy than biological brains. Neuromorphic systems aim to overcome this difference by mimicking the brain’s information coding via discrete voltage spikes. However, it remains unclear how both artificial and natural networks of spiking neurons can learn energy-efficient information processing strategies. Here we propose Predictive Coding Light (PCL), a recurrent hierarchical spiking neural network for unsupervised representation learning. In contrast to previous predictive coding approaches, PCL does not transmit prediction errors to higher processing stages. Instead it suppresses the most predictable spikes and transmits a compressed representation of the input. Using only biologically plausible spike-timing based learning rules, PCL reproduces a wealth of findings on information processing in visual cortex and permits strong performance in downstream classification tasks. Overall, PCL offers a new approach to predictive coding and its implementation in natural and artificial spiking neural networks.

Suggested Citation

  • Antony W. N’dri & Thomas Barbier & Céline Teulière & Jochen Triesch, 2025. "Predictive Coding Light," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-64234-z
    DOI: 10.1038/s41467-025-64234-z
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    References listed on IDEAS

    as
    1. Mingfang Zhang & Raluca Chitic & Sander M Bohté, 2025. "Energy optimization induces predictive-coding properties in a multi-compartment spiking neural network model," PLOS Computational Biology, Public Library of Science, vol. 21(6), pages 1-23, June.
    2. Cristina Savin & Prashant Joshi & Jochen Triesch, 2010. "Independent Component Analysis in Spiking Neurons," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-10, April.
    3. Valentina Baruzzi & Giacomo Indiveri & Silvio P. Sabatini, 2025. "Recurrent models of orientation selectivity enable robust early-vision processing in mixed-signal neuromorphic hardware," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
    4. repec:plo:pcbi00:1003258 is not listed on IDEAS
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