eLife Assessment: Fast and slow synaptic plasticity enables concurrent control and learning
Bibliographic record
Abstract
During many tasks the brain receives real-time feedback about performance. What should it do with that information, at the synaptic level, so that tasks can be performed as well as possible? The conventional answer is that it should learn by incrementally adjusting synaptic strengths. We show, however, that learning on its own is severely suboptimal. To maximize performance, synaptic plasticity should also operate on a much faster timescale – essentially, the synaptic weights should act as a control signal. We propose a normative plasticity rule that embodies this principle. In this, fast synaptic weight changes greedily suppress downstream errors, while slow synaptic weight changes implement statistically optimal learning. This enables near-perfect task performance immediately, efficient task execution on longer timescales, and confers robustness to noise and other perturbations. Applied in a cerebellar microcircuit model, the theory explains longstanding experimental observations and makes novel testable predictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".