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Record W2170766653 · doi:10.1109/iscas.2009.5117989

128-channel fully differential digital neural recording and stimulation interface

2009· article· en· W2170766653 on OpenAlexafffund
Farzaneh Shahrokhi, Karim Abdelhalim, Roman Genov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsCMOSChannel (broadcasting)Successive approximation ADCComputer scienceNoise (video)Bandwidth (computing)Electronic engineeringSIGNAL (programming language)Analog signalInterface (matter)Electrical engineeringEngineeringComputer hardwareDigital signal processingVoltageTelecommunicationsArtificial intelligenceComparator

Abstract

fetched live from OpenAlex

We present a fully differential 128-channel integrated neural interface. It consists of an array of 8times16 low-power low-noise signal recording and generation channels for electrical neural activity monitoring and stimulation, respectively. The recording channel has two stages of signal amplification and conditioning with a programmable gain of 54 dB to 73 dB, and a fully differential 8-bit column-parallel successive approximation (SAR) analog-to-digital converter (ADC). The design is implemented in a 0.35 mum CMOS technology with the channel pitch of 200 mum. The total measured power consumption of each recording channel including the SAR ADC is 15.5 muW. The measured input referred noise is 6.08 muV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rms</sub> over a 5 kHz bandwidth.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.804
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.207
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2009
Admission routes2
Has abstractyes

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