Transitions without change: How the perceived nature of shifts in neural field activity may be due to viewer perspective
Bibliographic record
Abstract
OBJECTIVE: An important goal of experimental and clinical neural field recordings is the identification of mechanisms that underlie transitions in persistent activity. Here we model such activity in order to identify the fundamental factors that may trigger transitions in dynamics. METHODS: We simulated laminar networks of spiking neurons with up to 10,000 inhibitory and excitatory units and varying degrees of connectivity. RESULTS: Activity in networks with heterogeneous connectivity showed various patterns of persistent activity including propagating waves. When activity was averaged over the population to simulate field recordings the dynamics showed ongoing changes in both amplitude and spectral properties. These perceived changes were present despite the absence of alteration to the intrinsic properties of the units or network structure. CONCLUSIONS: The observation that perceived shifts in spectral properties can be the simple result of averaging spatial propagation may have important implications for the interpretation of field recordings. It is often assumed that transitions in dynamics are due to (a) shifts in neural properties (b) changes to network structure (c) external input or (d) noise. The observation that intermittent transitions can take place in the absence of such factors suggests that we must look beyond these assumptions and on to the spatio‐temporal features of neural 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 | 0.008 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".