Balancing and tight coupling: an approach to determine dynamic mechanisms of biological brain networks
Bibliographic record
Abstract
Without a doubt, it is an extremely challenging endeavour to understand how our brains work.Oscillatory output, as produced by brain networks, has been shown to be important for brain functioning.Due to the high degree of sophistication and technical expertise required in experimentation, modeling, computation and analyses, it is clear that to move forth in our understanding, open and interactive collaborations between several individuals and disciplines are required.In this talk, I will discuss our developing approach to determine essential features and mechanisms for the generation of rhythmic, population output in microcircuits of the hippocampus.Due to its importance in learning and memory, as well as its association with pathological conditions, the hippocampus is a heavily studied brain structure.Furthermore, evidence is accumulating that pathological states are associated with particular changes in normal rhythmic activities.Through collaborative efforts, we have developed and are developing cellular and network models with tight experimental linkages.We are using them to identify critical cellular and synaptic aspects of dynamic mechanisms that can be examined in biological microcircuits.Overall, we aim to use our models to determine dynamic mechanisms used by biological microcircuits (from which one could consider building macrocircuits) and to use them to gain insight into disease mechanisms.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".