Inga D. Neumann: Molecular underpinnings of the brain oxytocin system and its involvement in socio-emotional behaviour: More than a love story
Bibliographic record
Abstract
Professor Inga Neumann stands at the forefront of neuropeptide research, bringing over three decades of expertise to her role as Chair of the Department of Behavioural and Molecular Neurobiology at the University of Regensburg, Germany. Her journey in science began in East Germany at the Karl-Marx-University in Leipzig (now the University of Leipzig), where she earned both her diploma in biology and her PhD. After the fall of the Berlin Wall, her career path led her through a postdoctoral position at the University of Calgary in Canada and seven enriching years at the Max-Planck Institute for Psychiatry in Munich before assuming her current position at Regensburg in 2001. As the first woman to be appointed full professor at the Faculty of Biology and Preclinical Medicine, she has shaped the University's neuroscience landscape by establishing and directing the Elite Masters Programme in Experimental and Clinical Neuroscience. Currently, she heads the Graduate School “Neurobiology of Socio-Emotional Dysfunctions,” a prestigious program funded by the German Research Foundation since 2017. The heart of her research lies in understanding how neuropeptides, particularly oxytocin, vasopressin, and CRF, orchestrate stress responses and social behaviours. Her work spans multiple levels of analysis – from molecular mechanisms and epigenetics to neural circuits and behaviour – primarily using rodent models to unlock the mysteries of the social brain. In this Genomic Press Interview, Professor Neumann shares her reflections on a life dedicated to unravelling the intricate relationships between brain chemistry and behaviour, offering insights into both her scientific journey and personal philosophy.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".