Electrophysiological Methods for Studying Ionic Currents in Brain Slices and Cell Cultures
Bibliographic record
Abstract
The last two decades have seen a tremendous explosion in our knowledge concerning the properties of neurons of the central nervous system (CNS). The basis of this explosion is twofold: one is the development of the methodology to maintain isolated slices of brain tissue alive, and the second is the discovery of pharmacological tools to alter specific ionic channels of neurons. Some pioneering work describing neuronal electrophysiology has also been done on neurons cultured in slice preparations or after dissociation. The purpose of this chapter is to briefly describe the methodologies involved in maintaining isolated brain slices and cultures of both slices and dissociated cells and to describe the experimental paradigms that have been developed in the last few years to determine the ionic currents underlying the electrophysiological properties of the mammalian CNS. Also, another important area of research will be discussed: the measurement of extracellular ion concentrations in the CNS and the dynamic changes that occur during neuronal activity. Much of this work has been done in vivo, but extracellular and intracellular ion measurements have also been made in brain slice preparations, Because of the complexity of the intact CNS, there are few experiments correlating extracellular ionic changes with intracellular ionic currents. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.014 |
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".