Overcoming sensory-memory interference in working memory circuits
Bibliographic record
Abstract
ABSTRACT Memories of recent stimuli are crucial for guiding behavior, but the sensory pathways responsible for encoding these memories are continuously bombarded by new sensory experiences. How the brain overcomes interference between sensory input and working memory representations remains largely unknown. To formalize the solution space, we examined recurrent neural networks that were either hand-designed or trained using gradient descent methods, and compared these models with neural data from two different macaque experiments. Here we report mechanisms by which neural networks overcome sensory-memory interference using both static and dynamic coding strategies: gating of the sensory inputs, modulating synapse strengths to achieve a strong attractor solution, and dynamic strategies – including the extreme solution in which cells invert their feature preference during working memory. Neural data from the medial superior temporal (MST) area of macaques, where sensory and working memory signals first interact along the dorsal pathway, best aligned with a solution that combined input gating and tuning inversion. Behavioral predictions from this model also matched error patterns observed in monkeys performing a working memory task with distractors. Taken together, our results help elucidate how working memory circuits preserve information as we continue to interact with the world, and suggest intermediate cortical visual areas like MST may play a critical role in this computation.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".