ArticlePatterns (New York, N.Y.)2022
Predictive coding is a consequence of energy efficiency in recurrent neural networks.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Flexible navigation with neuromodulated cognitive maps.PLoS computational biology · 2026Article
- Faster but less precise: expectation enhances response speed while reducing sensory fidelity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Categorization is 'baked' into the brain.Nature reviews. Neuroscience · 2026Review
- Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.PLoS computational biology · 2026Article
- Dynamic thermodynamic-informational entropic relationship (TIER) models of selective vulnerability to neurodegeneration.bioRxiv : the preprint server for biology · 2026Article
- 'Backpropagation and the brain' realized in cortical error neuron microcircuits.PLoS computational biology · 2026Article
- Insights into effortful adaptation during speech perception: The role of dopamine, norepinephrine, and prediction error.Cognitive, affective & behavioral neuroscience · 2026Review
- Predictive remapping and allocentric coding as consequences of energy efficiency in recurrent neural network models of active vision.Patterns (New York, N.Y.) · 2026Article
- Conductance-based reversal potentials in spiking recurrent neural networks enhance energy efficiency and task performance.Frontiers in computational neuroscience · 2026Article
- Top-down perceptual inference shaping the activity of early visual cortex.Nature communications · 2025Article
- Predictive Coding Light.Nature communications · 2025Article
- End-to-end topographic networks as models of cortical map formation and human visual behaviour.Nature human behaviour · 2025Article
- Article
- Dynamics of energy-efficient coding in visual cortex.Journal of neurophysiology · 2025Article
- Energy optimization induces predictive-coding properties in a multi-compartment spiking neural network model.PLoS computational biology · 2025Article
- Coordinating multiple mental faculties during learning.Scientific reports · 2025Article
- Dynamics of specialization in neural modules under resource constraints.Nature communications · 2025Article
- Model-Based Approaches to Investigating Mismatch Responses in Schizophrenia.Clinical EEG and neuroscience · 2025Review
- Multimodal evidence challenges the effectiveness of probabilistic cueing for establishing sensory expectations.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition.PLoS computational biology · 2025Article
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5 authors.
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Abstract
Predictive coding is a promising framework for understanding brain function. It postulates that the brain continuously inhibits predictable sensory input, ensuring 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 modeling to demonstrate that such architectural hardwiring is not necessary. Rather, predictive coding is shown to emerge as a consequence of energy efficiency. When training recurrent neural networks to minimize their energy consumption while operating in predictive environments, the networks self-organize 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.