Synaptic Scaling and the Development of a Motor Network
Bibliographic record
Abstract
Neurons respond homeostatically to chronic changes in network activity with compensatory changes such as a uniform alteration in the size of miniature postsynaptic current (mPSC) amplitudes termed synaptic scaling. However, little is known about the impact of synaptic scaling on the function of neural networks in vivo. We used the embryonic zebrafish to address the effect of synaptic scaling on the neural network underlying locomotion. Activity was decreased during development by TTX injection to block action potentials or CNQX injection to block glutamatergic transmission. Alternatively TNFalpha was chronically applied. Recordings from spinal neurons showed that glutamatergic mPSCs scaled up approximately 25% after activity reduction and fortuitously scaled down approximately 20% after TNFalpha treatment, and were unchanged following blockade of neuromuscular activity alone with alpha-bungarotoxin. Regardless of the direction of scaling, immediately following reversal of treatment no chronic effect was distinguishable in motoneuron activity patterns or in swimming behavior. We also acutely induced a similar increase of glutamatergic mPSC amplitudes using cyclothiazide to reduce AMPA receptor desensitization or decrease of glutamatergic mPSC amplitudes using a low concentration of CNQX to partially block AMPA receptors. Though the strength of the motor output was altered, neither chronic nor acute treatments disrupted the patterning of synaptic activity or swimming. Our results show, for the first time, that scaling of glutamatergic synapses can be induced in vivo in the zebrafish and that synaptic patterning is less plastic than synaptic strength during development.
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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".