Learning to combine top-down context and feed-forward representations under ambiguity with apical and basal dendrites
Bibliographic record
Abstract
One of the hallmark features of neocortical anatomy is the presence of extensive top-down projections into primary sensory areas. It is hypothesized that one of the roles of these top-down projections is to carry contextual information that helps animals to resolve ambiguities in sensory data. One proposed mechanism of contextual integration is a combination of input streams at distinct apical and basal dendrites of pyramidal neurons. Computationally, however, it is yet to be demonstrated how such an architecture could leverage distinct compartments for flexible contextual integration and sensory processing. Here, we implement a deep neural network with distinct apical and basal compartments that integrates (a) contextual information from top-down projections to apical compartments and (b) sensory representations driven by bottom-up projections to basal compartments. In addition, we develop a new contextual integration task using generative modeling. The performance of deep neural networks augmented with our "apical prior" exceeds that of single-compartment networks. We find that a sparse subset of neurons of the context-relevant categories receive the largest top-down signals. We further show that this sparse gain modulation is necessary. Altogether, this suggests that the "apical prior" could be key for handling the ambiguities that animals encounter in the real world.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".