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Record W2064117904 · doi:10.1145/1601896.1601901

Multichannel intracortical neurorecording

2009· article· en· W2064117904 on OpenAlexafffund
Mohamad Sawan, Benoit Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsPolytechnique Montréal
FundersCanada Research ChairsCMC Microsystems
KeywordsComputer scienceMicrosystemFlexibility (engineering)Reliability (semiconductor)Massively parallelComputer hardwareBrain implantEmbedded systemWirelessTransducerElectrode arrayPower (physics)Electrical engineeringVoltageEngineeringTelecommunicationsMaterials scienceArtificial intelligenceNanotechnology

Abstract

fetched live from OpenAlex

This paper covers an overview of multichannel massively parallel biosensing device dedicated for neural recording from the cortex. Attention is paid to describe the design techniques and assembly methods of high reliability Microsystems. These devices are optimized, first from the circuit level, to meet ultra low-power budget and all needed flexibility for efficient neurorecording. Second, at the system level, integration of multichips and their interconnections on top of electrode arrays represent challenging tasks to optimize the building of implantable intracortical sensors. Such device includes electrode arrays, preamplification front-end, data conversion, signal processing for the detection of neural events (spikes). It includes also multiple wireless links, the first one is for affording the energy to power up the whole implant, while data are exchanged bidirectionaly with a base station through two other links for data down and up links. Experimental results are shown.

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)0.001
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: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.038
GPT teacher head0.275
Teacher spread0.237 · 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 designBench or experimental
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

Citations1
Published2009
Admission routes2
Has abstractyes

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