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Record W1503041265 · doi:10.1109/iscas.2015.7168994

A wireless multichannel optogenetic headstage with on-the-fly spike detection

2015· article· en· W1503041265 on OpenAlexaff
G. Turcotte, C.-O. Dufresne Camaro, Alireza Avakh Kisomi, Reza Ameli, Benoit Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsOptogeneticsSpike (software development)Computer scienceWirelessSIGNAL (programming language)WaveformPower (physics)Battery (electricity)Electrical engineeringVoltageEngineeringTelecommunicationsPhysicsNeuroscience

Abstract

fetched live from OpenAlex

In this paper, we present a light-weight, wireless optogenetic headstage which provides optical neural stimulation and electrophysiological recording alongside on-the-fly neural signal processing. The proposed headstage is suitable to conduct long terms in-vivo experiments with small freely moving transgenic rodents, and features two implantable LED-coupled optical fibers and two electrophysiological recording channels while being powered by a small Lithium-ion battery. The headstage can transmit the raw neuronal signals or only spike waveforms after applying on-the-fly spike detection, which reduces power consumption by up to 14.5%. The headstage is entirely built using commercial off-the-shelf components, and the miniature design, using rigid-flex PCBs, results into a lightweight (7.4g) and compact device (25×20×15 mm). Low-power consumption is achieved by using on-the-fly spike detection alongside a real-time operating system which brings the headstage autonomy to 3h25 in full operation, including high-output power optical stimulation, micro-volts neuronal signal amplification and wireless transmission of the acquired waveforms.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.613

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 head0.132
GPT teacher head0.310
Teacher spread0.178 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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