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Record W4293010445 · doi:10.11159/icnf22.131

Self-closing Micro-Cuff for Neural Recording in the Locust

2022· article· en· W4293010445 on OpenAlexvenueno aff
Hasti Sajedi, Navjot S. Randhawa, Dhara Parikh, Haleh Fotowat, Fabrizio Gabbiani, J. C Wolfe

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2022
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsClosing (real estate)LocustComputer scienceBiologyEcologyBusiness

Abstract

fetched live from OpenAlex

We have developed a self-wrapping flexible cuff electrode for recording electrical activity on the descending contralateral movement detector (DCMD) neuron of the locust. First, a gold film is sputtered onto a monoaxially oriented polycarbonate membrane. Then, the resulting bilayer film is cut into individual strips parallel to the machine direction of the polycarbonate film. Thermally shrinking the polycarbonate at 155 0 C actuates the curl. This method can be used to fabricate controlled diameters between 50-250 m; diameter is controlled by thickness of the two layers and the temperature and duration of the heat shrinking process. The cuffs are selfclosing, and do not require suturing for attachment to a nerve. The cuffs are carefully unrolled, a gold film deposited, and a plasmadeposited, ion-sensitive resist applied. A stencil mask is then used to define the metallization lines by 50 kV He + ion beam proximity lithography. Ion milling transfers the resist pattern to the metal film. Conformality of the resist and large depth-of-field of the lithography are key to high resolution patterning on these non-flat substrates. The dielectric overcoat is a layer of exposed resist, which simplifies the opening of the contact windows, and provides excellent biocompatibility.

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 score0.074
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.283
Teacher spread0.246 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on New TechnologiesSame topicTactile and Sensory InteractionsFrench-language works237,207