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Record W4410845774 · doi:10.1088/1361-6463/adde6d

Asymmetrical response of NbO<sub>2</sub>-based neuristor for three-dimensional sound localization

2025· article· en· W4410845774 on OpenAlexafffund
Yungang Li, Yan Liu, Wenbo Sun, Christy Giji Jenson, Sharif Md. Sadaf, Juan Song, Yiwen Zhang, Xinjun Liu

Bibliographic record

VenueJournal of Physics D Applied Physics · 2025
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsSound (geography)AcousticsComputer sciencePhysics

Abstract

fetched live from OpenAlex

Abstract Neuristors show promise in replicating human-brain neuronal functions, yet their application to sensory neurons, especially for mimicking complex auditory behaviors like sound localization, remains challenging. This study uncovers an asymmetrical response in the output signals of NbO 2 -based neuristor: positive current inputs induce periodic oscillations, while negative inputs of equal magnitude trigger bursting spikes. A dual-input system with interaural time differences is presented for sound localization. The output waveform’s oscillation-bursting sequence indicates the sound source’s position on the left/right of the ear. Moreover, the oscillation’s amplitude and frequency can reveal the sound-source distance, and the number of bursting spikes determines the azimuth. Based on these findings, we propose a four-ear input neuronal circuit using two neuristor models for rapid and accurate two-dimensional spatial localization. For three-dimensional (3D) localization, a six-ear input model enables high-precision localization through octant determination, azimuth calculation, and frequency-based distance measurement. This research holds great potential for advancements in sound localization, 3D speed measurement, and voiceprint-based sound identification.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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 designSimulation or modeling
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

Citations2
Published2025
Admission routes2
Has abstractyes

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