Impact of open surface area of multi-well microelectrode array on mammalian brain cells recording efficiency
Bibliographic record
Abstract
Neuro-electrophysiology allows scientists to investigate the underlying electrical properties that constitute brain neural network assembly. Developing tools to study these properties is a rapidly-evolving research field, and recent advancements in micro electrode arrays (MEAs) is opening a new frontier in long-term data acquisition. MEA microfabrication techniques have advanced over the years and led to different types of electrodes. The objective of this study was to optimize MEA design featuring multiple wells per electrodes (MW-MEA), to improve the recording efficiency of MEAs used for in vitro electrophysiological recordings. Methods: Two multi-well electrode designs (5 wells with diameter of 20 μm vs. 6 wells with diameter of 15 μm) were evaluated. Peak to peak signal amplitude of the recorded signals and the noise levels were studied and the signal to noise ratios (SNR) were determined. Results: The signal amplitudes recorded by electrodes with 6 wells (1060.3 μm2 ) was higher than those recorded by 5-wells electrodes (1570.8 μm2 ), while the noise level remained identical in both designs (31.3 μv ± 10.2). As such, the SNR recorded by the 6-wells electrodes showed a 1.8-time increase, compared to the electrodes with 5 wells, although the diameter of the wells in the former design was smaller. The results of this study demonstrated an inverse relation between SNR and open surface area of electrodes. Significance: The identified relation between electrode well characteristics and MW-MEA performance and design optimization can improve signals’ resolution during long-term spontaneous extracellular recordings and thus the quality of brain cell activity recordings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".