A model for delta rhythm fit to high-frequency cortical activity data
Bibliographic record
Abstract
Rhythmic activity in the brain has been known since Berger's discovery of the alpha rhythm in the 1920's. Numerous mechanisms have been proposed for various rhythms but in the past half-century no consensus has been reached on the mechanism of any major rhythm. The recent development of high-throughput imaging methods enable us for the first time to rigorously and quantitatively test ideas about the dynamics of brain rhythms. The aim of this project is to characterize the contributions of intrinsic dynamics of brain regions and network connections in generating the global dynamics of cortical activity using a mouse model. We have generated high-resolution data on neural activity over 40 mm of mouse cortex by voltage-sensitive dyes, in both anesthetized and awake animals. We have instantiated current ideas about delta rhythm in models for the dynamics of such activity and we measure the fit of these models quantitatively in predicting this data, thus shedding light on the relative contributions of these processes in the delta rhythm. We include in the model intrinsic regional oscillations, thalamic input, and communication between cortical regions. We specify the form of the model and then estimate the parameters by fitting the dynamical behavior to high-resolution time series of cortical activity in mouse cortex. We try to estimate the relative contributions of each of the major components of the model to the fluctuations. We show that a potassium-current mechanism for intrinsic oscillations does in fact fit the data well, and we estimate some of the effective connectivity between different cortical regions under anesthesia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".