Experience Alters the Timing Rules Governing Synaptic Plasticity and Learning
Bibliographic record
Abstract
ABSTRACT The brain learns about the statistical relationships between events in the world through associative synaptic plasticity, controlled by the timing between neural events. Here, we show that experience can dramatically alter the timing rules governing associative plasticity and learning. In normally reared mice, the timing requirements for short- and long-term associative plasticity at synapses in the oculomotor cerebellum are precisely matched to the 120 ms delay for visual feedback to the circuit about behavioral errors. This specialization of the plasticity rules for the constraints of a particular circuit and learning task is acquired through experience. In dark-reared mice that never experienced visual feedback about oculomotor errors, synapses defaulted to a coincidence-based plasticity rule, with a corresponding delay in the timing of learned eye movements. This temporal metaplasticity persists into adulthood; when mice reared normally from birth were moved to dark housing as adults, the task-specific timing requirements for plasticity and the temporal accuracy of learning were lost and then re-established when visual experience was restored. Computational modeling suggests two general classes of biologically plausible mechanisms, each with multiple possible implementations, that can tune plasticity to distinct features of the statistics of neural activity. Temporal metaplasticity provides a potentially general mechanism for experience-dependent improvement in the way a circuit solves the “temporal credit assignment problem” inherent in most learning tasks, thereby providing a candidate synaptic mechanism for meta-learning.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".