#### Filter Results:

- Full text PDF available (9)

#### Publication Year

2011

2016

- This year (0)
- Last 5 years (10)
- Last 10 years (12)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Brain Region

#### Cell Type

#### Key Phrases

#### Method

#### Organism

Learn More

- Javier DeFelipe, Pedro L López-Cruz, +39 authors Giorgio A Ascoli
- Nature reviews. Neuroscience
- 2013

A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian… (More)

- Gherardo Varando, Pedro L. López-Cruz, Thomas D. Nielsen, Pedro Larrañaga, Concha Bielza
- Int. J. Intell. Syst.
- 2015

Mixtures of polynomials (MoPs) are a non-parametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn oneand multi-dimensional (marginal) MoPs from data have recently been proposed. In this paper we introduce two methods for learning MoP approximations of conditional… (More)

- Concha Bielza, Ruth Benavides-Piccione, Pedro López-Cruz, Pedro Larrañaga, Javier DeFelipe
- Scientific reports
- 2014

Unraveling pyramidal cell structure is crucial to understanding cortical circuit computations. Although it is well known that pyramidal cell branching structure differs in the various cortical areas, the principles that determine the geometric shapes of these cells are not fully understood. Here we analyzed and modeled with a von Mises distribution the… (More)

- Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga, Ruth Benavides-Piccione, Javier DeFelipe
- Neuroinformatics
- 2011

Neuron morphology is crucial for neuronal connectivity and brain information processing. Computational models are important tools for studying dendritic morphology and its role in brain function. We applied a class of probabilistic graphical models called Bayesian networks to generate virtual dendrites from layer III pyramidal neurons from three different… (More)

- Pedro L. López-Cruz, Pedro Larrañaga, Javier DeFelipe, Concha Bielza
- Int. J. Approx. Reasoning
- 2014

Article history: Available online 2 April 2013

- Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga
- Int. J. Approx. Reasoning
- 2014

- Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga
- CAEPIA
- 2011

Directional and angular information are to be found in almost every field of science. Directional statistics provides the theoretical background and the techniques for processing such data, which cannot be properly managed by classical statistics. The von Mises distribution is the best known angular distribution. We extend the naive Bayes classifier to the… (More)

- Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga
- Pattern Analysis and Applications
- 2013

Directional data are ubiquitous in science. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. We extend the naive Bayes classifier… (More)

- Pedro L. López-Cruz, Thomas D. Nielsen, Concha Bielza, Pedro Larrañaga
- CAEPIA
- 2013

Mixtures of polynomials (MoPs) are a non-parametric density estimation technique for hybrid Bayesian networks with continuous and discrete variables. We propose two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the… (More)

- L. López-Cruz, Pedro Nielsen, +4 authors Pedro Larrañaga
- 2016

Mixtures of polynomials (MoPs) are a non-parametric density estimation technique for hybrid Bayesian networks with continuous and discrete variables. We propose two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the… (More)