Coupling of neurons with biosensor devices for detection of the properties of neuronal populations
Bibliographic record
Abstract
The in vitro detection of the neural biophysical chemistry of populations of neurons is an important emerging area of research. This critical review describes the current methodologies, challenges and future prospects for this exciting field of research. There are different classes of techniques for the study of neuron-based systems. These include devices to measure inter-neuron contact and connectivity, microelectrodes for the determination of extracellular metabolic products, and sensors employed for the evaluation of complex neuron-small molecule interactions, toxicity, and mutagenicity of anti-tumor drugs. Since the neuron is an electrogenic cell and a complex biological entity capable of effecting recognition, the main emphasis of this article will be placed on devices based on nerve-cell networks that are able to electrically detect neuron-active compounds and specific pharmacological activity. Such neuron-based devices can be used to measure numerous neurological events with a high degree of sensitivity. Examples include the influence of different neuro-active compounds on neuronal function, the effects of neurotransmitters and neuro-modulators, changes in membrane potential, transmission effects that influence the propagation of the action potential, and the manner through which neuro-chemicals can influence ion channels. Moreover, these devices posses promising potential for the testing and development of novel neuron-active drugs and fundamental neurological research to further the understanding of brain activity. The inner workings of the human mind remain largely unknown and the key to comprehending it may rely on how molecules can initiate and influence synchronous neural oscillations, and the phenomenon of resonance in neural cells. The knowledge acquired in such detailed investigations can lead to the future development of regenerative medicines, neurochips and biocomputers, intelligent prosthetic devices and new applications that integrate neurobiology with molecular electronics (69 references).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".