Improving a cortical pyramidal neuron model's classification performance on a real-world ecg dataset by extending inputs.
Biological Neural Networks
ECG Data
Machine Learning
Neural Dynamics
Pyramidal Neurons
Synaptic Inputs
Journal
Journal of computational neuroscience
ISSN: 1573-6873
Titre abrégé: J Comput Neurosci
Pays: United States
ID NLM: 9439510
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
received:
10
10
2022
accepted:
25
04
2023
revised:
23
04
2023
medline:
8
8
2023
pubmed:
6
5
2023
entrez:
6
5
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 37148455
doi: 10.1007/s10827-023-00851-1
pii: 10.1007/s10827-023-00851-1
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
329-341Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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