ArticleNature machine intelligence2022
Neurons learn by predicting future activity.
Article in Nature machine intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 83 citations in OpenAlex.
- A Perovskite Memdiode-Based Neuromorphic in Silico Surrogate Model for Emulating Predictive Coding Failure in Diabetic Neuropathy.Neuroinformatics · 2026Article
- Categorization is 'baked' into the brain.Nature reviews. Neuroscience · 2026Review
- Mechanistic explanation of neuroplasticity using equivalent circuits.Frontiers in computational neuroscience · 2026Article
- Neuroscience of Behavior.NeuroSci · 2025Review
- Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences.eLife · 2025Article
- Shapley Fields Reveal Chemotopic Organization in the Mouse Olfactory Bulb Across Diverse Chemical Feature Sets.bioRxiv : the preprint server for biology · 2025Article
- Entropy of Neuronal Spike Patterns.Entropy (Basel, Switzerland) · 2024Review
- Article
- Neuron-level Prediction and Noise can Implement Flexible Reward-Seeking Behavior.bioRxiv : the preprint server for biology · 2024Article
- Network Representation of fMRI Data Using Visibility Graphs: The Impact of Motion and Test-Retest Reliability.Neuroinformatics · 2024Article
- Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex.PLoS computational biology · 2024Article
- Determination of Neuronal Activity and Its Meaning for the Processes of Learning and Memory.Advances in neurobiology · 2024Review
- Unsupervised learning on spontaneous retinal activity leads to efficient neural representation geometry.ArXiv · 2023Article
- Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule.Nature communications · 2023Article
- Prediction in cultured cortical neural networks.PNAS nexus · 2023Article
- A time-causal and time-recursive scale-covariant scale-space representation of temporal signals and past time.Biological cybernetics · 2023Review
- Biologically-inspired neuronal adaptation improves learning in neural networks.Communicative & integrative biology · 2023Article
- Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network.Frontiers in computational neuroscience · 2023Review
- Learning cortical hierarchies with temporal Hebbian updates.Frontiers in computational neuroscience · 2023Article
- Editorial: Deciphering population neuronal dynamics: from theories to experiments.Frontiers in systems neuroscience · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 2 countries.
Funding
Abstract
Understanding how the brain learns may lead to machines with human-like intellectual capacities. It was previously proposed that the brain may operate on the principle of predictive coding. However, it is still not well understood how a predictive system could be implemented in the brain. Here we demonstrate that the ability of a single neuron to predict its future activity may provide an effective learning mechanism. Interestingly, this predictive learning rule can be derived from a metabolic principle, where neurons need to minimize their own synaptic activity (cost), while maximizing their impact on local blood supply by recruiting other neurons. We show how this mathematically derived learning rule can provide a theoretical connection between diverse types of brain-inspired algorithms, thus, offering a step toward development of a general theory of neuronal learning. We tested this predictive learning rule in neural network simulations and in data recorded from awake animals. Our results also suggest that spontaneous brain activity provides "training data" for neurons to learn to predict cortical dynamics. Thus, the ability of a single neuron to minimize surprise: i.e. the difference between actual and expected activity, could be an important missing element to understand computation in the brain.
Identifiers
What OpenQuestion holds
Registered trials
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.