A brain-wide, trial- and time-dependent deterministic drive synergizes with within-trial noise to time self-initiated actions
Bibliographic record
Abstract
Deciding when to act in the absence of external cues is essential for exploration, learning, and survival. Yet the neural mechanisms underlying such decisions remain controversial, with current views favoring either deterministic or stochastic underpinnings. We simultaneously recorded from large neuronal populations in cortical, thalamic, pallidal, and cerebellar regions as mice self-initiated voluntary actions. Action onset timing was predictable from firing patterns up to several seconds in advance with predictions correlated across regions, demonstrating a prominent deterministic drive that spans regions. Computational modeling indicated that this drive has an initial value and rate that vary trial-by-trial, and the rate increases within trials. Although the deterministic drive is sufficient to trigger action, noise within trials also contributes to setting action timing. Therefore, discrete (across-trial) and continuous (within-trial) sources of variability synergize to time self-initiated actions. This synergy is observed brain-wide, suggesting a distributed decision-making process rather than a hierarchical, modular one.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".