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K-Winners-Take-All Computation with Neural Oscillators

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Wei Wang, Jean-Jacques E. Slotine

Neurons and Cognition · Submitted 31 Dec 2003

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Fast convergence is achieved from arbitrary initial conditions, which makes the networks particularly suitable to track time-varying inputs.

how it is stated · asserted 1.00 read the record →

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arXiv IDq-bio/0401001 (version 1)
Pith IDJYIQA2IK, Pith's permanent record of this paper and its review
CitationJean-Jacques E. Slotine, Wei Wang. "K-Winners-Take-All Computation with Neural Oscillators." arXiv:q-bio/0401001 (2003). https://pith.science/paper/JYIQA2IK
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,919 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.14 · human participants 0.02 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.17 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Artificial spike-based computation, inspired by models of computation in the central nervous system, may present significant performance advantages over traditional methods for specific types of large scale problems. This paper describes very simple network architectures for k-winners-take-all and soft-winner-take-all computation using neural oscillators. Fast convergence is achieved from arbitrary initial conditions, which makes the networks particularly suitable to track time-varying inputs.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Artificial spike-based computation, inspired by models of computation in the central nervous system, may present significant performance advantages over traditional methods for specific types of large scale problems. background 1.00 0.07 0.09 hedged possibility 1.00
s1 This paper describes very simple network architectures for k-winners-take-all and soft-winner-take-all computation using neural oscillators. contribution 0.92 0.88 0.25 asserted 0.99
s2 Fast convergence is achieved from arbitrary initial conditions, which makes the networks particularly suitable to track time-varying inputs. result 0.86 0.82 0.31 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.99 · evidence from simulation or computation 0.56.

What was examined, and what was not

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  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Artificial spike-based computation, inspired by models of computation in the central nervous system, may present significant performance advantages over traditional methods for specific types of large scale problems. This paper describes very simple network architectures for k-winners-take-all and soft-winner-take-all computation using neural oscillators. Fast convergence is achieved from arbitrary initial conditions, which makes the networks particularly suitable to track time-varying inputs.

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