Phase‐coupled oscillator models can predict hippocampal inhibitory synaptic connections
Bibliographic record
Abstract
What factors are responsible for propagating electrical activity in the hippocampus? Using an intact, isolated hippocampus preparation, it is possible to observe spontaneous delta (< or = 4 Hz) waves of rhythmic field potentials. These rhythmic potentials are inhibitory in nature, mediated by GABAergic inhibitory potentials originating from a population of principal neurons. They start in the ventro-temporal region and move longitudinally towards the dorso-septal region with a phase lag of approximately 10% between the extracellular recordings. We use the mathematical framework of phase-coupled oscillators (PCO) to gain some insight into the underlying network system. A chain of 15 nearest-neighbour bidirectionally coupled PCOs is used where each oscillator refers to a segment of the CA1 region of the hippocampus that can generate these slow field potentials. We find that ventro-dorsal delta waves exist if there is a dominance in coupling strength in one direction. Without a one-way coupling dominance, ventro-dorsal waves can still exist, but then the coupling strengths need to be much larger. The relationship between entrained and intrinsic frequencies and the variation of propagation speeds along the longitudinal axis can be used to determine which case applies. Currently available experimental data supports one of the cases, predicting that there is a stronger ventral to dorsal inhibitory effect.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".