A programmable platform for sub-second multichemical dynamic stimulation and neuronal functional imaging in<i>C. elegans</i>
Bibliographic record
Abstract
Caenorhabditis elegans (C. elegans) is a prominent model organism in neuroscience, as its small stereotyped nervous system offers unique advantages for studying neuronal circuits at the cellular level. Characterizing temporal dynamics of neuronal circuits is essential to fully understand neuronal processing. Characterization of the temporal dynamics of chemosensory circuits requires a precise and fast method to deliver multiple stimuli and monitor the animal's neuronal activity. Microfluidic platforms have been developed that offer an improved control of chemical delivery compared to manual methods. However, stimulating an animal with multiple chemicals at high speed is still difficult. In this work, we have developed a platform that can deliver any sequence of multiple chemical reagents, at sub-second resolution and without cross-contamination. We designed a network of chemical selectors wherein the chemical selected for stimulation is determined by the set of pressures applied to the chemical reservoirs. Modulation of inlet pressures has been automated to create robust, programmable sequences of subsecond chemical pulses. We showed that stimulation with sequences of different chemicals at the second to sub-second range can generate different neuronal activity patterns in chemosensory neurons; we observed previously unseen neuronal responses to a controlled chemical stimulation. Because of the speed and versatility of stimulus generated, this platform opens new possibilities to investigate neuronal 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 | 0.000 |
| 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 | 0.001 | 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".