A model of neurobiologically plausible least-squares learning in visual cortex
Bibliographic record
Abstract
We show mathematically how a pseudo-inverse approach to training an artificial neural network for classification using a mean square error loss function and large batches of training data can be recast as an iterative method that circuits of real neurons in visual cortex could plausibly learn in an online manner. We argue the case for neurobiological plausibility based on our illustration that the iterative method can be reformulated as an unsupervised stage that learns to decorrelate using an anti-Hebbian learning rule, and a supervised stage that follows simple Hebbian learning while retaining mean square error optimality. Importantly, we modify the baseline method to ensure the learning rules rely on information that would be available locally at synapses if the method were instantiated in a network of cortical neurons. We demonstrate results comparable on par or better than similar iterative methods applied to the MNIST hand-written digits image classification benchmark, and the related but more challenging EMNIST database. The proposed learning algorithm learns quickly, reaching good accuracy with a small amount of training 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".