A 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu\text{m}$ </tex-math> </inline-formula> CMOS SoC for Simultaneous Multichannel Optogenetics and Neural Recording
Bibliographic record
Abstract
This paper presents a 0.13-μm CMOS system-onchip (SoC) for simultaneous multichannel optogenetics and multichannel neural recording in freely moving laboratory animals. This fully integrated system provides 10 multimodal recording channels with analog-to-digital conversion and a fourchannel LED driver circuit for optogenetic stimulation. The bio-amplifier design includes a programmable bandwidth (BW) (0.5 Hz-7 kHz) to collect either the action potentials (APs) and/or the local field potentials (LFPs) and has a noise efficiency factor (NEF) of 2.30 for an input-referred noise of 3.2 μVrms within a BW of 10-7 kHz. The low-power delta-sigma (ΔΣ) MASH 1-1-1 analog-to-digital converter (ADC) is designed to work at low oversampling ratios (OSRs) (≤50) and has an effective number of bits (ENOB) of 9.75 bits at an OSR of 25 (BW of 10 kHz). The utilization of a ΔΣ ADC is the key to address the flexibility needed to address different noise versus power consumption tradeoff of various experimental settings. It leverages a new technique that reduces its size by subtracting the output of each ΔΣ branch in the digital domain, instead of in the analog domain as done conventionally. The ADC is followed by an on-chip fourthorder cascaded integrator-comb (CIC4) decimation filter (DF). A whole recording channel, including the bio-amplifier, the ΔΣ MASH 1-1-1, and the DF consumes 11.2 μW. Optical stimulation is performed with an LED driver using a regulated cascode current source with feedback that can accommodate a wide range of LED parameters and battery voltages. The SoC is validated in vivo within a wireless experimental platform in both the ventral posteromedial nucleus (VPM) and cerebral motor cortex brain regions of a virally mediated Channelrhodopsin-2 (ChR2) rat.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.031 |
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".