Neuro-Headstage with On-Chip Machine Learning and Wireless Charging for Closed-Loop Neurobiological Applications
Bibliographic record
Abstract
This paper presents a novel wireless electrooptic headstage for long-term, autonomous neurobiological experiments. The system features a custom mixed-signal SoC, fabricated in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.13-\mu \mathrm{m}$</tex> CMOS technology, and enables lowlatency closed-loop neural stimulation (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\leq 0.6 ~\text{ms}$</tex>) triggered by complex neural firing patterns. A wireless inductive power link supports continuous operation in freely moving models without frequent recharging. At its core, the Adaptive Autonomous Neural Integrated Circuit (AANIC) provides dual site optogenetic stimulation and multi-unit electrophysiology recording (10 channels), allowing simultaneous stimulation and recording across brain regions. The system offers a tunable bandwidth <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(1-6500 ~\text{Hz})$</tex> and high-fidelity signal processing, achieving an ENOB of 8.68 with a Delta-Sigma converter at an OSR of 25. In vivo tests demonstrate successful seizure suppression in a mouse model of temporal lobe epilepsy via closed-loop activation of ChR2-expressing inhibitory interneurons. In addition, wireless charging functionality was validated in freely moving mice, marking a step toward chronic studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".