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Predictive coding is a consequence of energy efficiency in recurrent neural networks

Abdullahi Ali, Nasir Ahmad, Elgar de Groot, Marcel A. J. van Gerven, View ORCID ProfileTim C. Kietzmann
doi: https://doi.org/10.1101/2021.02.16.430904
Abdullahi Ali
1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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  • For correspondence: a1.ali@donders.ru.nl t.kietzmann@donders.ru.nl
Nasir Ahmad
1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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Elgar de Groot
1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
2Department of Experimental Psychology, Utrecht University, Utrecht, the Netherlands
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Marcel A. J. van Gerven
1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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Tim C. Kietzmann
1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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  • ORCID record for Tim C. Kietzmann
  • For correspondence: a1.ali@donders.ru.nl t.kietzmann@donders.ru.nl
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Abstract

Predictive coding represents a promising framework for understanding brain function. It postulates that the brain continuously inhibits predictable sensory input, ensuring a preferential processing of surprising elements. A central aspect of this view is its hierarchical connectivity, involving recurrent message passing between excitatory bottom-up signals and inhibitory top-down feedback. Here we use computational modelling to demonstrate that such architectural hard-wiring is not necessary. Rather, predictive coding is shown to emerge as a consequence of energy efficiency. When training recurrent neural networks to minimise their energy consumption while operating in predictive environments, the networks self-organise into prediction and error units with appropriate inhibitory and excitatory interconnections, and learn to inhibit predictable sensory input. Moving beyond the view of purely top-down driven predictions, we demonstrate via virtual lesioning experiments that networks perform predictions on two timescales: fast lateral predictions among sensory units, and slower prediction cycles that integrate evidence over time.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/KietzmannLab/EmergentPredictiveCoding

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 16, 2021.
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Predictive coding is a consequence of energy efficiency in recurrent neural networks
Abdullahi Ali, Nasir Ahmad, Elgar de Groot, Marcel A. J. van Gerven, Tim C. Kietzmann
bioRxiv 2021.02.16.430904; doi: https://doi.org/10.1101/2021.02.16.430904
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Predictive coding is a consequence of energy efficiency in recurrent neural networks
Abdullahi Ali, Nasir Ahmad, Elgar de Groot, Marcel A. J. van Gerven, Tim C. Kietzmann
bioRxiv 2021.02.16.430904; doi: https://doi.org/10.1101/2021.02.16.430904

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