The Neural Code for Pain: From Single-Cell Electrophysiology to the Dynamic Pain Connectome
Bibliographic record
Abstract
Pain occurs in time. In naturalistic settings, pain perception is sometimes stable but often varies in intensity and quality over the course of seconds, minutes, and days. A principal aim in classic electrophysiology studies of pain was to uncover a neural code based on the temporal patterns of single neuron firing. In contrast, modern neuroimaging studies have placed emphasis on uncovering the spatial pattern of brain activity (or "map") that may reflect the pain experience. However, in the emerging field of connectomics, communication within and among brain networks is characterized as intrinsically dynamic on multiple time scales. In this review, we revisit the single-cell electrophysiological evidence for a nociceptive neural code and consider how those findings relate to recent advances in understanding systems-level dynamic processes that suggest the existence of a "dynamic pain connectome" as a spatiotemporal physiological signature of pain. We explore how spontaneous activity fluctuations in this dynamic system shape, and are shaped by, acute and chronic pain experiences and individual differences in those experiences. Highlighting the temporal dimension of pain, we aim to move pain theory beyond the concept of a static neurosignature and toward an ethologically relevant account of naturalistic dynamics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".