Evidence map›Paper›PMID 37148455›Full record

ArticleJournal of computational neuroscience2022

Improving a cortical pyramidal neuron model's classification performance on a real-world ecg dataset by extending inputs.

Ilknur Kayikcioglu Bozkir, Zubeyir Ozcan, Cemal Kose, Temel Kayikcioglu, Ahmet Enis Cetin

Abstract read
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Article in Journal of computational neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Ilknur Kayikcioglu BozkirDepartment of Computer Engineering, Karadeniz Technical University, Trabzon, Türkiye. ilknurkayikcioglu@ktu.edu.tr.
Zubeyir OzcanDepartment of Electrical and Electronics Engineering, Karadeniz Technical University, Trabzon, Türkiye.
Cemal KoseDepartment of Computer Engineering, Karadeniz Technical University, Trabzon, Türkiye.
Temel KayikciogluDepartment of Electrical and Electronics Engineering, Karadeniz Technical University, Trabzon, Türkiye.
Ahmet Enis CetinDepartment of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pyramidal neurons display a variety of active conductivities and complex morphologies that support nonlinear dendritic computation. Given growing interest in understanding the ability of pyramidal neurons to classify real-world data, in our study we applied both a detailed pyramidal neuron model and the perceptron learning algorithm to classify real-world ECG data. We used Gray coding to generate spike patterns from ECG signals as well as investigated the classification performance of the pyramidal neuron's subcellular regions. Compared with the equivalent single-layer perceptron, the pyramidal neuron performed poorly due to a weight constraint. A proposed mirroring approach for inputs, however, significantly boosted the classification performance of the neuron. We thus conclude that pyramidal neurons can classify real-world data and that the mirroring approach affects performance in a way similar to non-constrained learning.

Indexed as

Models, NeurologicalPyramidal CellsElectrocardiographyNeural Networks, ComputerNeuronsBiological Neural NetworksECG DataMachine LearningNeural DynamicsPyramidal NeuronsSynaptic Inputs

Identifiers

PMID37148455

What OpenQuestion holds

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Registered trials

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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.