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Record W2018000085 · doi:10.1109/biocas.2011.6107715

Low-power energy-based CMOS digital detector for neural recording arrays

2011· article· en· W2018000085 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsPolytechnique Montréal
FundersDivision of Materials ResearchCMC MicrosystemsNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMcGill University
KeywordsThresholdingComputer scienceDetectorCMOSBandwidth (computing)Channel (broadcasting)Artificial neural networkEnergy consumptionEnergy (signal processing)Electronic engineeringComputer hardwareArtificial intelligenceElectrical engineeringTelecommunicationsEngineeringMathematics

Abstract

fetched live from OpenAlex

Recent research works in wireless neural recording systems by microelectrode arrays favor spikes extraction to limit the required bandwidth. While simple thresholding locates spikes, using an adequate pre-processor before thresholding can improve the performances of detection. We present in this paper low-power implementations of three interesting energy-based preprocessors (Abs, TEO, and Smoothed-TEO). The proposed novel spike detection module allows a trade-off between silicon area and power consumption of the system. Performances have been evaluated with recorded neural signals from monkeys to determine the optimal trade-off. The post-routed power estimation showed that the implementation of the optimal detection pre-processor tested in this work, the Smoothed-TEO, achieves 961 nW per channel and occupies a silicon area of 0.008 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> per channel.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.579
Threshold uncertainty score0.559

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 head0.023
GPT teacher head0.205
Teacher spread0.183 · 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

Quick stats

Citations4
Published2011
Admission routes2
Has abstractyes

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