Assembly of a functional neuronal circuit in embryos of an ancestral metazoan is influenced by temperature and the microbiome
Bibliographic record
Abstract
Understanding how neural populations emerge to give rise to behavior is a major goal in neuroscience. Here, we explore the self-assembly of neural circuits in Hydra , an organism with a simple nervous system but no centralized information processing, to enhance the understanding of nervous system evolution. We define self-assembly as spontaneous organization of neurons into functional circuits without requiring a prespecified structural template. In this context, the N4 neuronal circuit, which we have previously found to be particularly important in the feeding of the animal, develops in embryos through activity-driven self-assembly, a process in which intrinsic calcium activity drives connectivity and synchronization among spatially distributed neurons over time. Gap junctions and vesicle-mediated communication between neuronal and non-neuronal cells drive rapid assembly, with the embryo’s prospective oral region exhibiting the highest neuronal density. An artificial electrical circuit–based model as a biophysically inspired simulation demonstrates dynamic increases in synchronization over time, along with predictions for selective dynamic adaptions of connections. Environmental factors, like temperature and an absent microbiome, modify neural architecture, suggesting the existence of a certain adaptability during neural development. We propose that these fundamental features originated in the last common bilaterian ancestor, supporting the hypothesis that the basic architecture of the nervous system is universal. Since in the natural habitat of Hydra both temperature fluctuations and changes in the microbiome can occur, our work not only illuminates a fundamental developmental process but also may guide environmental and evolutionary studies by explaining how organisms adapt to environmental variations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".