Spontaneous traveling waves are an intrinsic feature of ongoing cortical dynamics and regulate perceptual sensitivity
Bibliographic record
Abstract
Psychophysics has long focused on measuring the stimulus intensity that reaches the threshold for conscious report. However, variability in the neural activity evoked by the stimulus results in variable perceptual sensitivity for the same level of stimulus intensity. These variable fluctuations in neural activity have therefore been regarded as a source of noise, impairing the threshold for conscious perception estimated from signal detection theory. Recently we have found that variable fluctuations in cortical responses are due, in part, to the state of traveling waves of spontaneous cortical activity (Davis et al., Nature, 2020). These waves modulate stimulus-evoked spiking activity and perceptual sensitivity in marmosets trained to detect faint visual targets. Thus, in contrast to the traditional view of fluctuations as harmful noise, traveling waves improve perceptual thresholds. To gain insight into the mechanisms underlying traveling waves, we study a large-scale spiking network model with conductance-based synapses, biologically realistic topographic connectivity, and action potential propagation speeds consistent with those observed in unmyelinated horizontal fibers. We found that these properties were sufficient to generate spontaneous waves across the entire range of network parameters that produced asynchronous-irregular spiking dynamics (Brunel, J Comput Neurosci, 2000; Renart et al., Science, 2010). Further, we found that neuronal participation in these waves was sparse, enabling traveling waves to coexist with asynchronous-irregular spiking activity without necessarily inducing correlations, which have been found to impair perception (Nandy et al., eLife, 2019). This sparse-wave network regime remained sensitive to feed-forward input and modulated the strength of stimulus-evoked responses as observed in the cortex. This was in contrast to networks that produced dense spiking waves, which drove strong correlations and rendered the network insensitive to feed-forward input. Traveling waves appear to be an intrinsic feature of cortical dynamics, and they therefore likely impact the moment-to-moment processing of information throughout the brain.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".