Evidence map›Paper›PMID 27726048›Full record

ArticleJournal of computational neuroscience2017

Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.

Brandon S Coventry, Aravindakshan Parthasarathy, Alexandra L Sommer, Edward L Bartlett

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of computational neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Brandon S CoventryWeldon School of Biomedical Engineering, Purdue University, Purdue, USA.
Aravindakshan ParthasarathyWeldon School of Biomedical Engineering, Purdue University, Purdue, USA.
Alexandra L SommerWeldon School of Biomedical Engineering, Purdue University, Purdue, USA.
Edward L BartlettWeldon School of Biomedical Engineering and the Department of Biological Sciences, Purdue University, Purdue, USA. ebartle@purdue.edu.

Funding

Measurement and modeling of central auditory processing in aged animalsR01DC011580 · NIDCD · PURDUE UNIVERSITY · PI BARTLETT, EDWARD · 2012 to 2016
$1.7M
NIDCD NIH HHS R01 DC011580
6 · The paper itself

Abstract

Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.

Indexed as

AlgorithmsComputer SimulationSocial SupportAcoustic StimulationModels, NeurologicalNeuronsBiological neural networksEvolutionary computationModel optimizationParticle swarm optimization

Identifiers

PMID27726048
PMCPMC5253113

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.