Dendritic branching angles of pyramidal cells across layers of the juvenile rat somatosensory cortex

Leguey, Ignacio; Bielza, Concha; Larrañaga, Pedro; Kastanauskaite, Asta; Rojo, Carmen; Benavides-Piccione, Ruth; DeFelipe, Javier

doi:10.1002/cne.23977

Cited by 7 publications

(12 citation statements)

References 42 publications

(62 reference statements)

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“…We applied our classifiers to a dataset of 3027 combinations of dendritic bifurcation angles coming from the basal arbors of 288 3D pyramidal neurons in layers II, III, IV, Va, Vb and VI (48 neurons per layer) of the 14-day-old (P14) rat hind limb somatosensory (S1HL) neocortex, recently published in [14] (Fig. 6).…”

Section: Real Data Examplementioning

confidence: 99%

“…Thus, we developed a classification model to predict which layer a given neuron belongs to, i.e., ( ) = ( , , , , , ). Following the notation used in [14], 1 will correspond to the first bifurcation angle (Order 1) generated for the first split of the dendritic segments starting from the soma. The second angle generated by the next consecutive splits will be represented as variable 2 (Order 2), etc.…”

Section: Real Data Examplementioning

confidence: 99%

“…Circular data is ubiquitous, present in many different areas such as biology, geology, medicine, oceanography, geophysics, meteorology, astronomy, ecology, neuroscience and geography. Some examples are directions of flight of homing pigeons [1], characterization of the phenology of species [2], formation of feldspar laths in basalt rocks [3], paleomagnetism in red slits and claystones [4,5], also in political sciences studying the gun crimes occurred in an specific period of time [6], directional word vectors in text mining [7], wildfire orientation in order to prevent fire propagation [8], wind and waves direction analysis [9,10], study and prediction of protein dihedral angles structure [11,12], and neuronal basal dendritic bifurcation angles analysis [13,14] among many others. The natural periodicity of circular data sometimes makes traditional statistics methods ineffective, since they ignore this characteristic.…”

Section: Introductionmentioning

confidence: 99%

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Circular Bayesian classifiers using wrapped Cauchy distributions

Leguey

Bielza

Larrañaga

2019

Data & Knowledge Engineering

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A B S T R A C TCapturing the dependences among circular variables within supervised classification models is a challenging task. In this paper, we propose four different supervised Bayesian classification algorithms where the predictor variables follow all circular wrapped Cauchy distributions. For this purpose, we introduce four wrapped Cauchy classifiers. The bivariate wrapped Cauchy distribution is the only bivariate circular distribution whose marginals and conditionals are also wrapped Cauchy distributions, a property that makes it possible to define these models easily. Furthermore, the wrapped Cauchy tree-augmented naive Bayes classifier requires the definition of a conditional circular mutual information measure between variables that follow wrapped Cauchy distributions. Synthetic data is used to illustrate, compare and evaluate the classification algorithms (including a comparison with the Gaussian TAN classifier, decision tree, random forest, multinomial logistic regression, support vector machine and simple neural network), leading to satisfactory predictive results. We also use a real neuromorphological dataset obtained from juvenile rat somatosensory cortex cells, where we measure the bifurcation angles of the dendritic basal arbors.characteristic that each variable is conditionally independent of those that are non-descendants in the graph given the value of their parents. Therefore the joint probability distribution is expressed as the product of the local distributions conditioned to their parents. For these reasons, Bayesian networks can deal efficiently with supervised classification i.e., the Bayesian network classifiers [13] and offer an explicit, graphical and interpretable representation of uncertain knowledge, which has made it possible to successfully apply them to real-world problems.Supervised classification [19] deals with the problem of assigning a label to an instance, based on a set of variables that characterize it. Yet circular data has been commonly treated as linear data in supervised classification tasks. Only a few circular classifiers exist, and almost none of them are based on the principles of Bayesian networks, capable of capturing multivariate relationships among variables. Most of them focus on discriminant analysis and assume several circular distributions such as the von Mises distribution [20], later extended to the von Mises-Fisher distribution [21]. There are also circular discriminant analysis studies for the Watson, Selby and Arnold distributions on the sphere [22,23]. SenGupta and Roy [24] used a classification discriminant rule based on the mean chord-length to classify a new observation into one of two different circular populations that are von Mises, when training samples are available for each of them. Also a likelihood ratio test based on a bootstrapping approach for classifying into two populations was proposed for linear and circular data [25]. Kirby and Miranda [26] proposed a variation of a neural network, including a circular node, which was able to ke...

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Section: Real Data Examplementioning

confidence: 99%

Section: Real Data Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Circular Bayesian classifiers using wrapped Cauchy distributions

Leguey

Bielza

Larrañaga

2019

Data & Knowledge Engineering

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“…Since our main application interest is in neuroscience, we applied our proposal to a real-world dataset of 3027 combinations of dendritic bifurcation angles com posed by 288 3D pyramidal neurons from six different layers of the 14-day-old (P14) rat hind limb somatosensory (S1HL) neocortex recently published in [13]. Dendritic bifurcation angles are an important part of the geometry of pyramidal cell basal arbors.…”

Section: Real Data Examplementioning

confidence: 99%

“…Each angle is generated by two sib ling segments originating from the bifurcation of basal dendritic trees. Using the same notation as in [13], we denote the first bifurcation that takes place in a dendritic arbor path that starts from the soma and ends at the angle as O1, the second bifurcation would be O2, etc. (Fig.…”

Section: Real Data Examplementioning

confidence: 99%

Tree-Structured Bayesian Networks for Wrapped Cauchy Directional Distributions

Leguey

Bielza

Larrañaga

2016

Advances in Artificial Intelligence

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Dendritic-branching angles of pyramidal neurons of the human cerebral cortex

et al. 2016

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In this article, we analyze branching angles of the basal dendrites of pyramidal neurons of layers III and V of the human temporal cortex. For this, we use a novel probability directional statistical distribution called truncated von Mises distribution that is able to describe more accurately the dendritic-branching angles than the previous proposals. Then, we perform comparative studies using this statistical method to determine similarities and/or differences between branches and branching angles that belong to different cortical layers and regions. Using this methodology, we found that common design principles exist and govern the patterns found in the different branches that compose the basal dendrites of human pyramidal cells of the temporal cortex. However, particular differences were found between supra and infragranular cells. Furthermore, we compared the branching angles of human layer III pyramidal neurons with data obtained in the previous studies in layer III of both the rat somatosensory cortex and of several cortical areas of the mouse. Finally, we study the branching angle differences between the humans that compose our data.Electronic supplementary materialThe online version of this article (doi:10.1007/s00429-016-1311-0) contains supplementary material, which is available to authorized users.

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Dendritic branching angles of pyramidal cells across layers of the juvenile rat somatosensory cortex

Cited by 7 publications

References 42 publications

Circular Bayesian classifiers using wrapped Cauchy distributions

Circular Bayesian classifiers using wrapped Cauchy distributions

Tree-Structured Bayesian Networks for Wrapped Cauchy Directional Distributions

Dendritic-branching angles of pyramidal neurons of the human cerebral cortex

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