Whole Cell Recordings From Visualized Neurons in the Inner Laminae of the Functionally Intact Spinal Cord
Bibliographic record
Abstract
The in vitro whole spinal cord preparation has been an invaluable tool for the study of the neural network that underlies walking because it provides a means of recording fictive locomotor activity following surgical and/or pharmacological manipulation. The recent use of molecular genetic techniques to identify discrete neuronal populations in the spinal cord and subsequent studies showing some of these populations to be involved in locomotor activity have been exciting developments that may lead to a better understanding of the structure and mechanism of function of this neural network. It would be of great benefit if the in vitro whole spinal cord preparation could be updated to allow for the direct targeting of genetically defined neuronal populations, allowing each to be characterized physiologically and anatomically. This report describes a new technique that enables the visualization of, and targeted whole cell patch-clamp recordings from, genetically defined populations of neurons while leaving connectivity largely intact. The key feature of this technique is a small notch cut in the lumbar spinal cord that reveals cells located in the intermediate laminae while leaving the ventral portion of the spinal cord-the region containing the locomotor neural network-untouched. Whole cell patch-clamp recordings demonstrate that these neurons are healthy and display large rhythmic depolarizations that are related to electroneurogram bursts recorded from ventral roots during fictive locomotion. Intracellular labeling demonstrates that this technique can also be used to map axonal projection patterns of neurons. We expect that this procedure will greatly facilitate electrophysiological and anatomical study of important neuronal populations that constitute neural networks throughout the CNS.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".