Glial Regulation of Neuronal Function: From Synapse to Systems Physiology
Bibliographic record
Abstract
Classically, glia have been regarded as non-excitable cells that provide nourishment and physical scaffolding for neurones. However, it is now generally accepted that glia are active participants in brain function that can modulate neuronal communication via several mechanisms. Investigations of anatomical plasticity in the magnocellular neuroendocrine system of the hypothalamic paraventricular and supraoptic nuclei led the way in the development of much of our understanding of glial regulation of neuronal activity. In this review, we provide an overview of glial regulation of magnocellular neurone activity from a historical perspective of the development of our knowledge of the morphological changes that are evident in the paraventricular and supraoptic nuclei. We also focus on recent data from the authors' laboratories presented at the 9th World Congress on Neurohypophysial Hormones that have contributed to our understanding of the multiple mechanisms by which glia modulate the activity of neurones, including: gliotransmitter modulation of synaptic transmission; trans-synaptic modulation by glial neurotransmitter transporter regulation of neurotransmitter spillover; and glial neurotransmitter transporter modulation of excitability by regulation of ambient neurotransmitter levels and their action on extrasynaptic receptors. The magnocellular neuroendocrine system secretes oxytocin and vasopressin from the posterior pituitary gland to control birth, lactation and body fluid balance, and we finally speculate as to whether glial regulation of individual magnocellular neurones might co-ordinate population activity to respond appropriately to altered physiological circumstances.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".