Ruling out and ruling in neural codes
Bibliographic record
Abstract
The subject of neural coding has generated much debate. A key issue is whether the nervous system uses coarse or fine coding. Each has different strengths and weaknesses and, therefore, different implications for how the brain computes. For example, the strength of coarse coding is that it is robust to fluctuations in spike arrival times; downstream neurons do not have to keep track of the details of the spike train. The weakness, though, is that individual cells cannot carry much information, so downstream neurons have to pool signals across cells and/or time to obtain enough information to represent the sensory world and guide behavior. In contrast, with fine coding, individual cells can carry much more information, but downstream neurons have to resolve spike train structure to obtain it. Here, we set up a strategy to determine which codes are viable, and we apply it to the retina as a model system. We recorded from all the retinal output cells an animal uses to solve a task, evaluated the cells' spike trains for as long as the animal evaluates them, and used optimal, i.e., Bayesian, decoding. This approach makes it possible to obtain an upper bound on the performance of codes and thus eliminate those that are insufficient, that is, those that cannot account for behavioral performance. Our results show that standard coarse coding (spike count coding) is insufficient; finer, more information-rich codes are necessary.
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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 | 0.022 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".