ArticleRespiratory physiology & neurobiology2015
Automatic classification of canine PRG neuronal discharge patterns using K-means clustering.
Article in Respiratory physiology & neurobiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Fentanyl effects on respiratory neuron activity in the dorsolateral pons.Journal of neurophysiology · 2022Article
- Endogenous glutamatergic inputs to the Parabrachial Nucleus/Kölliker-Fuse Complex determine respiratory rate.Respiratory physiology & neurobiology · 2020Article
- Inspiring song: The role of respiratory circuitry in the evolution of vertebrate vocal behavior.Developmental neurobiology · 2020Review
- Inputs to medullary respiratory neurons from a pontine subregion that controls breathing frequency.Respiratory physiology & neurobiology · 2019Article
- Premotor Neuron Divergence Reflects Vocal Evolution.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2018Article
- Mapping Early, Middle and Late Rice Extent Using Sentinel-1A and Landsat-8 Data in the Poyang Lake Plain, China.Sensors (Basel, Switzerland) · 2018Article
- Characteristics of breathing rate control mediated by a subregion within the pontine parabrachial complex.Journal of neurophysiology · 2017Article
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Authors and funding
6 authors.
Funding
Abstract
Respiratory-related neurons in the parabrachial-Kölliker-Fuse (PB-KF) region of the pons play a key role in the control of breathing. The neuronal activities of these pontine respiratory group (PRG) neurons exhibit a variety of inspiratory (I), expiratory (E), phase spanning and non-respiratory related (NRM) discharge patterns. Due to the variety of patterns, it can be difficult to classify them into distinct subgroups according to their discharge contours. This report presents a method that automatically classifies neurons according to their discharge patterns and derives an average subgroup contour of each class. It is based on the K-means clustering technique and it is implemented via SigmaPlot User-Defined transform scripts. The discharge patterns of 135 canine PRG neurons were classified into seven distinct subgroups. Additional methods for choosing the optimal number of clusters are described. Analysis of the results suggests that the K-means clustering method offers a robust objective means of both automatically categorizing neuron patterns and establishing the underlying archetypical contours of subtypes based on the discharge patterns of group of neurons.
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