Sensor Technology in Neuroscience
Bibliographic record
Abstract
Biosensor technology has rapidly expanded into a wide variety of applications in the last few years. Such fields include clinical diagnostics, environmental chemistry, drug discovery and pathogen detection, to name but a few. The structure of these sensors is based on the intimate combination of a biological entity with a transducer capable of generating an electrical signal to provide information on the biological system being studied. Until now there has been a limited treatment of the study of whole cells (as a biological component) due to the difficulty in connecting transducers to cell populations. This book focuses on several aspects of neural behaviour both in vitro and in vivo, and for the first time, the detection of populations of neurons (rather than single cells) will be presented. The fundamental behaviour and characterization of neurons on various substrates, using a variety of electronic devices such as transistors and microelectrode arrays will be discussed. Future perspectives discussed in the book include artificial intelligence using biological neural networks and nanoneuromedicine. The authors have considerable experience in biosensor technology, and have pioneered the study of neural populations using biosensors in collaboration with neurophysiologists and neuroendrocrinologists. This book will be invaluable to university neuroscience and analytical chemistry departments and students, academics and physicians will benefit from its accessible style and format.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.023 |
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".