Comprehensive imaging of synaptic activity reveals dendritic growth rules that cluster inputs
Bibliographic record
Abstract
Abstract The distribution of synapses across dendritic arbors determines their contribution to neural computations since nonlinear conductances amplify co-active clustered inputs. To determine whether, and how patterned synaptic topography arises during development we developed a random-access microscope capable of full-neuron calcium imaging of activity and structural plasticity of developing neurons in awake Xenopus tadpoles. By imaging growing brain neurons in response to plasticity-inducing visual training, we show coordinated growth and synaptogenesis specific to each neuron’s spike tuning. High evoked activity in neurons tuned to the trained stimulus induced pruning of non-driven inputs across the dendritic arbor as these neurons strengthened their responses to this stimulus. In stark contrast, initially unresponsive neurons that shifted their spike tuning toward the trained stimulus exhibited localized growth and new responsive synapses near existing active inputs. These information-driven growth rules promote clustering of synapses tuned to a developing neuron’s emerging receptive field. One-Sentence Summary Sensory input directs brain neuronal growth and connectivity promoting clustering of synaptic inputs tuned to a neuron’s encoding properties.
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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 | 0.000 |
| 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".