Clonal Relationships Impact Neuronal Tuning within a Phylogenetically Ancient Vertebrate Brain Structure
Bibliographic record
Abstract
Understanding how neurons acquire specific response properties is a major goal in neuroscience. Recent studies in mouse neocortex have shown that "sister neurons" derived from the same cortical progenitor cell have a greater probability of forming synaptic connections with one another and are biased to respond to similar sensory stimuli. However, it is unknown whether such lineage-based rules contribute to functional circuit organization across different species and brain regions. To address this question, we examined the influence of lineage on the response properties of neurons within the optic tectum, a visual brain area found in all vertebrates. Tectal neurons possess well-defined spatial receptive fields (RFs) whose center positions are retinotopically organized. If lineage relationships do not influence the functional properties of tectal neurons, one prediction is that the RF positions of sister neurons should be no more (or less) similar to one another than those of neighboring control neurons. To test this prediction, we developed a protocol to unambiguously identify the daughter neurons derived from single tectal progenitor cells in Xenopus laevis tadpoles. We combined this approach with in vivo two-photon calcium imaging in order to characterize the RF properties of tectal neurons. Our data reveal that the RF centers of sister neurons are significantly more similar than would be expected by chance. Ontogenetic relationships therefore influence the fine-scale topography of the retinotectal map, indicating that lineage relationships may represent a general and evolutionarily conserved principle that contributes to the organization of neural circuits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".