Inferring plasticity rules from single-neuron spike trains using deep learning methods
Bibliographic record
Abstract
Abstract Synaptic plasticity is a core basis for learning and adaptation. Determining how synapses are altered by local signals – the learning rules – is the hinge about which brain activity pivots. A large number of in vitro characterizations have focused on restricted sets of core properties, but it remains to be established which if any of the known learning rules is most consistent with changes in activity patterns in behaving animals. To address this question, we hypothesize that the correlation between features of the activity of a single post-synaptic neuron and subsequent changes of the representations could be used to detect the underlying learning rule. Because this correlation is expected to be diluted in the notoriously large variability of brain activity, we test here learning rule inference based on passive observations of single neurons using deep artificial neural networks. Using simulated data, we found that both transformers, temporal convolutional networks, and SVM could classify learning rules far above the chance level, with transformers achieving the best overall accuracy. This performance can be achieved despite the presence of noise and representational drift. We further investigated the features used by the algorithms to perform the classification and found the deep net used inner temporal differences of distinct learning rules to separate learning trajectories. We also find, however, that the classification accuracy is sensitive to alterations in network properties. Our work illustrates that distinct learning rules’ generate distinguishable trajectories of responses, but warns against using simulation-trained classifiers to infer learning rules from real data.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".