Neurons learn by predicting future activity
Bibliographic record
Abstract
Abstract Understanding how the brain learns may lead to machines with human-like intellectual capacities. However, learning mechanisms in the brain are still not well understood. Here we demonstrate that the ability of a neuron to predict its future activity may provide an effective mechanism for learning in the brain. We show that comparing a neuron’s predicted activity with the actual activity provides a useful learning signal for modifying synaptic weights. 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. This reveals an unexpected connection that learning in neural networks could result from simply maximizing the energy balance by each neuron. We show how this mathematically derived learning rule can provide a theoretical connection between diverse types of brain-inspired algorithms, such as: Hebb’s rule, BCM theory, temporal difference learning and predictive coding. Thus, this may offer a step toward development of a general theory of neuronal learning. We validated this predictive learning rule in neural network simulations and in data recorded from awake animals. We found that in the sensory cortex it is indeed possible to predict a neuron’s activity ∼10-20ms into the future. Moreover, in response to stimuli, cortical neurons changed their firing rate to minimize surprise: i.e. the difference between actual and expected activity, as predicted by our model. Our results also suggest that spontaneous brain activity provides “training data” for neurons to learn to predict cortical dynamics. Thus, this work demonstrates that the ability of a neuron to predict its future inputs could be an important missing element to understand computation in the brain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".