The Effects of Population Tuning and Trial-by-Trial Variability on Information Encoding and Behavior
Bibliographic record
Abstract
Identifying the features of population responses that are relevant to the amount of information encoded by neuronal populations is a crucial step toward understanding population coding. Statistical features, such as tuning properties, individual and shared response variability, and global activity modulations, could all affect the amount of information encoded and modulate behavioral performance. We show that two features in particular affect information: the modulation of population responses across conditions (population signal) and the inverse population covariability along the modulation axis (projected precision). We demonstrate that fluctuations of these two quantities are correlated with fluctuations of behavioral performance in various tasks and brain regions consistently across 4 monkeys (1 female and 1 maleMacaca mulatta; and 2 maleMacaca fascicularis). In contrast, fluctuations in mean correlations among neurons and global activity have negligible or inconsistent effects on the amount of information encoded and behavioral performance. We also show that differential correlations reduce the amount of information encoded in finite populations by reducing projected precision. Our results are consistent with predictions of a model that optimally decodes population responses to produce behavior. SIGNIFICANCE STATEMENTThe last two or three decades of research have seen hot debates about what features of population tuning and trial-by-trial variability influence the information carried by a population of neurons, with some camps arguing, for instance, that mean pairwise correlations or global fluctuations are important while other camps report opposite results. In this study, we identify the most important features of neural population responses that determine the amount of encoded information and behavioral performance by combining analytic calculations with a novel nonparametric method that allows us to isolate the effects of different statistical features. We tested our hypothesis on 4 macaques, three decision-making tasks, and two brain areas. The predictions of our theory were in agreement with the experimental 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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".