Spontaneous traveling waves naturally emerge from horizontal fiber time delays and travel through locally asynchronous-irregular states
Bibliographic record
Abstract
Abstract Sensory neuroscience has focused a great deal of its attention on characterizing the mean firing rate that is evoked by a stimulus, and while it has long been recognized that the firing rates of individual neurons fluctuate around the mean, these fluctuations are often treated as a form of internally generated noise1. There is, however, evidence that these “ongoing” fluctuations of activity in sensory cortex during normal, waking function shape neuronal excitability and responses to external input2,3. We have recently found that spontaneous fluctuations are organized into waves traveling at speeds consistent with the speed of action potentials traversing unmyelinated horizontal cortical fibers (0.1-0.6 m/s)4 across the cortical surface5. These waves systematically modulate excitability across the retinotopic map, strongly affecting perceptual sensitivity as measured in a visual detection task. The underlying mechanism for these waves, however, is unknown. Further, it is unclear whether waves are consistent with the low rate, highly irregular, and weakly correlated “asynchronous-irregular” dynamics observed in computational models6 and cortical recordings in vivo7. Here, we study a large-scale computational model of a cortical sheet, with connections ranging up to biological scales. Using an efficient custom simulation framework, we study networks with topographically-organized connectivity and distance-dependent axonal conduction delays from several thousand up to one million neurons. We find that spontaneous traveling waves are a general property of these networks and are consistent with the asynchronous-irregular regime. These waves are well matched to spontaneous waves recorded in the neocortex of awake monkeys. Further, individual neurons sparsely participate in waves, yielding a sparse-wave regime that offers a unique operating mode, where traveling waves coexist with locally asynchronous-irregular dynamics, without inducing deleterious neuronal correlations8.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".