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Record W4306827193 · doi:10.1088/2634-4386/ac9b85

Plasticity of conducting polymer dendrites to bursts of voltage spikes in phosphate buffered saline

2022· article· en· W4306827193 on OpenAlexaff
Corentin Scholaert, Kamila Janzakova, Yannick Coffinier, Fabien Alibart, Sébastien Pecqueur

Bibliographic record

VenueNeuromorphic Computing and Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsUniversité de Sherbrooke
FundersH2020 European Research Council
KeywordsBurstingPolarity (international relations)Dendrite (mathematics)InterconnectivityComputer scienceBiological systemNeuroscienceMaterials scienceBiophysicsChemistryBiologyArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Abstract The brain capitalizes on the complexity of both its biochemistry for neurons to encode diverse pieces of information with various neurotransmitters and its morphology at multiple scales to route different pathways for neural interconnectivity. Conducting polymer dendrites can show similar features by differentiating between cations and anions thanks to their charge accumulation profile and the asymmetry in their dendriticity that allows projecting spike signals differently. Here, we exploit such mimicry for in materio classification of bursting activity and investigate, in phosphate buffered saline, the capability of such object to sense bursts of voltage pulses of 100 mV amplitude, emitted by a local gate in the vicinity of the dendrite. The dendrite integrates the different activities with a fading memory time window that is characteristic of both the polarity of the spikes and the temporality of the burst. By this first demonstration, the ‘material-object’ definitely shows great potential to be a node halfway between the two realms of brain and electronic communication.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.027
GPT teacher head0.223
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 source (direct Gemma or distilled Codex), 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

Citations8
Published2022
Admission routes1
Has abstractyes

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