An Adaptive and Autonomous System-On-Chip with Data Analysis for μs-Latency Closed-Loop Optogenetics
Bibliographic record
Abstract
In this paper, we report a mixed-signal chip in 0.13-μm designed for long term autonomous operation that can trigger low-latency stimulation upon the detection of complex neural firing patterns. This adaptive autonomous neural IC (AANIC) includes: i) circuits for multi-site optogenetics stimulation and multi-unit electrophysiology recording, ii) specialized digital cores to detect, compress, and classify the neural signals in situ and iii) a programmable processor, connected to the specialized cores, running the closed-loop algorithm. The AANIC can process 10 neural channels in parallel, and stimulate through two independent precise current-sources, and is integrated within a tiny wireless optogenetic platform. The AANIC has a latency of 0.6 ms, a power of 165 μW/Ch, an effective number of bits (ENOB) of 8.68 bits (OSR=25), a tunable bandwidth (1-6.5 kHz), and is tested in vivo in freely-moving experiments involving a mouse model of temporal lobe epilepsy expressing ChR2 in its inhibitory neurons.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".