Implantable microsystem for concurrent measurement of brain's action potential and neurotransmitter
Bibliographic record
Abstract
Thanks to technological advancements in recent decades, outstanding progress has been made in the field of neuroscience and neural engineering. Although the information processing in the brain is mostly done through neuron's electrical activities, there might be significant information in presynaptic neurochemicals. Recent studies suggest concurrent measurement of interrelated brain's electrical and neurochemical activity may lead to better understanding of brain function in addition to developing optimal neural prosthetics. We present a power efficient implantable CMOS microsystem for simultaneous measurement of Action Potential (AP) and neurotransmitter concentration. It consist of a nano-power neural amplifier for action potential detection and amplification; a nano-power current conveyor potentiostat which senses picoscale to microscale current that corresponds to micromolar neurotransmitter concentration; and a micro-power ΣΔ Analog to Digital Convertor (ADC) to convert the analog signal (AP or neurotransmitter concentration) to digital code. This microsystem is fabricated in CMOS 0.18 μ technology and tested using recorded signals from dorsal premotor cortex (PMd) area of a macaque monkey in our lab.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".