The Cajal Blue Brain Project (CBBP) was approved in 2009 for a period of 10 years (until 2018). This project has made it possible to create a multidisciplinary team of more than 50 researchers (anatomists, physiologists, mathematicians and computer scientists). As a result of the CBBP, several tools and new computational methods have been developed that represent an important technological contribution to the study of the brain.

Motivation

One of the main goals of neuroscience is to understand the biological mechanisms responsible for human mental activity. In particular, the study of the cerebral cortex is and without any doubt will be the greatest challenge for science in the next centuries, since it represents the foundation of our humanity. In other words, the cerebral cortex is the structure whose activity is related to the capabilities that distinguish humans from other mammals. Thanks to the development and evolution of the cerebral cortex we are able to perform highly complex and specifically human tasks, such as writing a book, composing a symphony or developing technologies.

For these reasons the Blue Brain project emerged in 2005, when the L’Ecole Polytechnique Fédérale de Lausanne (Switzerland) and IBM jointly launched an ambitious project to create a functional brain model by means of reverse engineering of the mammalian brain, using the Blue Gene supercomputer from IBM. The aim was to understand the functioning and dysfunction of the brain through detailed simulations. By late 2006, the Blue Brain project had created a model of the basic functional unit of the brain, the neocortical column. However, the goals set by the project, which covered a period of 10 years, imposed its conversion into an international initiative (The Blue Brain Project, Nat Rev Neurosci. 7, 153-160, 2006). In this context, the Cajal Blue Brain project, the Spanish contribution to this international project, started in January 2009 led by the Universidad Politécnica de Madrid (UPM) and the Consejo Superior de Investigaciones Científicas (CSIC) .

Neuronal Forest simulation

Art and Technical Direction: Luis Pastor, Ángel Rodríguez, Susana Mata and Sofía Bayona - Art and Technical - Production and Development: Juan Pedro Brito and Luis Miguel Serrano - Technical Advice: José Miguel Espadero - Art Advice: Eva Cortés - Scientific Advice: Javier DeFelipe y Ruth Benavides-Piccione

"The garden of neurology offers the investigator captivating spectacles and incomparable artistic emotions. In it, my aesthetic instincts were at last full satisfied. Like the entomologist hunting for brightly colored butterflies, my attention was drawn to the flower garden of the gray matter that contained cells with delicate and elegant forms, the mysterious butterflies of the soul, the beating of whose wings may some day (who knows?) clarify the secret of mental life. […] Even from the aesthetic point of view, the nervous tissue contains the most charming attractions. In our parks is there any tree more elegant and luxurious than the Purkinje cell from the cerebellum or the psychic cell, that is the famous cerebral pyramid?"

Santiago Ramón y Cajal, 1894

Based on the idea that most connections are established by chemical point-to-point synapses, the terms ‘connectome’ and ‘synaptome’ have been proposed to facilitate the description of the maps of connections at different levels of resolution. The term connectome can be used to refer to maps at the macroscopic and mesoscopic levels, which also allows putative synaptic contacts to be mapped, while synaptome refers to the map of true synaptic contacts at the ultrastructural level (From the connectome to the synaptome: an epic love story, Science 330:1198-1201, 2010). Electron microscopy with serial section reconstruction is the gold standard method for tracing the connections. However, obtaining long series of sections is rather time-consuming and challenging. Consequently, the reconstruction of large tissue volumes is usually impossible.

The introduction of automated or semi-automated electron microscopy techniques at the turn of the century represented a major advance in the study of the synaptome as long series of consecutive sections can now be obtained with little user intervention. As this technology becomes more popular, it will have a huge impact on the study of the ultrastructure of the brain. Despite these high hopes, the principal drawback is that complete reconstructions of whole brains are only possible in some invertebrates or for relatively simple nervous systems, whereas for small mammals like the mouse, it is impossible to fully reconstruct the brain at the ultrastructural level. This is because the magnification needed to visualize and classify the synaptic junctions (i.e., excitatory and inhibitory) and to measure their sizes and shapes accurately enough yields relatively small images (in the order of tens of μm2). As a result, it is only possible to obtain incomplete synaptomes. It seems clear that only by combining studies at the macro-, meso-, and nano-scopic levels can we fully understand the structural arrangement of the brain as a whole.

Integration of microanatomical data. Schematic representation to show how we could deal with the problem of imprecise connectomes and incomplete synaptomes focusing on the cortical columns. A. Instead of reconstructing all cellular components within the column, the principles governing the structural design of cells can be obtained by using data from a few 3D reconstructed neurons and applying mathematical tools to determine the statistical structure of the neurons to computationally synthesize model neurons. The cells can be labeled with markers that allow full visualization of their dendritic and axonal arbors and then 3D reconstructed at the light microscope level, allowing the morphometric analysis of individual cells (i.e., patterns of dendritic arbors, distribution and density of dendritic spines, etc.). These data are also critical for modeling neuronal function such as synaptic integration in dendrites and dendritic spines. For instance, based on the dendritic spine distribution and their morphology (different colors represent different sizes), it is possible to generate maps of putative synaptic currents. B. Another set of structural data comes from measurements of the grey matter thickness, the volume fraction of cortical elements (neuropil, neurons, glia and blood vessels), neuron and glia density per volume, together with the patterns of local (intralaminar, translaminar) and long-range (cortico-cortical, thalamo-cortical, cortico-thalamic, subcortical extra-thalamic) connections. To determine the synaptic contribution of pyramidal cells in a given cortical layer, it is impractical to reconstruct all of these cells at the electron microscope level. Instead, this parameter could be inferred by combining quantitative light microscopy data on the total number and microanatomical characteristics of these cells on the one hand, with the average density of axo-spinous and axo-dendritic synapses obtained by analyzing multiple samples of the 3D reconstructed neuropil, using automated electron microscopy techniques and tools for Image analysis, segmentation, and quantification of different types of synapses (green, asymmetric synapses; red, symmetric synapses). Taken from Neuroanatomy and Global Neuroscience. Neuron. 2017 95:14-18, 2017.

It is important to emphasize that acquiring multiple samples at different scales (light and electron microscopy) allows us to obtain a dataset that can be statistically analyzed in search of general patterns of organization (Reconstruction and Simulation of Neocortical Microcircuitry. Cell 163:456-492, 201.). This multiple sampling approach assures unprecedented accuracy, since we obtain both precise quantitative data and statistical variability information. The data can be used to identify common and differing principles of organization and to develop algorithms to reconstruct synaptic connections for use in brain models (Figure Integration of microanatomical data).

"CeSViMa Supercomputer"

Furthermore, it seems that the most appropriate approach to make neuroanatomical studies more significant is to link detailed structural data with the incomplete light and electron microscopy wiring diagrams and integrate this neuroanatomical information with genetic, molecular and physiological data. This integration would allow the generation of models that present the data in a form that can be used to reason, make predictions and suggest new hypotheses to discover new aspects of the structural and functional organization of the brain (e.g., Reconstruction and Simulation of Neocortical Microcircuitry. Cell 163:456-492, 201).

One of the strengths of the Cajal Blue Brain project is that all the participating laboratories and research groups will be coordinated, so that all the effort will be channelled towards a specific objective, using strictly common methodological criteria. Thus, the data generated in a laboratory can be effectively used by other research groups. Definitively, the Cajal Blue Brain project is structured in such a way that it will work as a single, large multidisciplinary laboratory. In this way, the project will generate significant advances in our understanding of the structure and function of the normal brain.

The Project (2009-2018)

The project is markedly interdisciplinary in nature, requiring the collaboration of scientists from different fields. The long-term objectives of the Cajal Blue Brain can be summarized as follows:

Key Objectives:

To decode the synaptome or detailed map of the synaptic connections of the cortical column and, as a result, reconstruct all its components.
To give a strong boost to research on the cortical column, exploring in depth current hypotheses about its normal function and dysfunctions (especially Alzheimer’s disease)
To devise new methods to process and analyze the experimental data obtained in the aforementioned research studies.
To develop computer technology to study neuronal functions using graphics tools and visualization methods

Other Objectives:

To understand the implication of glial cells and blood vessels in the organization of the cortical column.
To study the modulation of the functional organization of the cerebral cortex by cortical and subcortical afferent connections.
To decipher the functional organization of cortical circuits in vitro and in vivo.
To simulate in silico the activity of the cortical column by means of a supercomputer.

As the Universidad Politécnica did not have a Neuroscience laboratory equipped with the tools and personnel required to become a world leader in the research proposed through this ambitious project and other related projects, the joint UPM-CSIC "Laboratorio Cajal de Circuitos Corticales or LCCC (Cajal Cortical Circuit Laboratory)" was created, which is part of the Centro de Tecnología Biomédica or CTB (Biomedical Technology) of the UPM Montegancedo Campus. The work carried out at the LCCC, the maintenance of this laboratory and the progressive acquisition of the most advanced tools and technologies is essential in order to obtain the neurobiological data required to meet the project’s objectives. The maintenance of the LCCC is therefore a priority in the Cajal Blue Brain project as it generates the knowledge which is the basis for the subsequent development of computer tools and data analysis methods the project requires.

Project Organization

BBP Structure

Project Management

Project Director: Prof. Javier DeFelipe (CCCL, UPM-CSIC)
Project Manager: Dr. Pilar Flores-Romero (CCCL, UPM-CSIC)

Coordinators of the Scientific Modules

Neuroscience: Prof. Javier DeFelipe (CCCL, UPM-CSIC)
Physiolgy and Functional Modelling: Prof. Óscar Herreras (IC CSIC)
Data Analysis: Prof. Pedro Larrañaga & Prof. Concha Bielza(CIG, UPM)
Neuroinformatics: Prof. Luis Pastor (CCS-UPM)
Visualization: Prof. Luis Pastor (GMRV, URJC)

External Collaborators

Prof. Alfonso Araque (IC, CSIC)
Prof. Rafael Luján (UCLM)

IT Tools

Espina

Since synapses are key elements in the structure of nervous circuits, understanding their location, size and proportion between the two different types is extraordinarily important in terms of function.

In this way, EspINA tool automatically performs segmentation and 3D volume reconstruction of synapses in the brain, helping the user to examine large tissue volumes and interactively validate the results provided by the software.

Morales J, Alonso-Nanclares L, Rodríguez J-R, DeFelipe J, Rodríguez Á and Merchán-Pérez Á (2011) ESPINA: a tool for the automated segmentation and counting of synapses in large stacks of electron microscopy images. Front. Neuroanat. 5:18. doi: 10.3389/fnana.2011.00018

EspINA can display multiple spatially or temporally related images. These image sets are called stacks. The images that make up a stack are called sections. All the sections in a stack must be the same size and bit depth. EspINA supports 8-bit images.

EspINA is a memory intensive application used to reconstruct, refine, analyze and visualize structures in the brain. It's an open source application and it's functionalities can be expanded by plugins. It's currently available for Linux (Ubuntu based) and Windows machines with the only requirement of a 64 bits CPU.

Audispine

We present a new method with musical feedback for exploring dendritic spine morphology and distribution patterns in pyramidal neurons. We demonstrate that audio analysis of spiny dendrites with apparently similar morphology may “sound” quite different, revealing anatomical substrates that are not apparent from simple visual inspection.

Pablo Toharia, Juan Morales, Octavio de Juan, Isabel Fernaud, Angel Rodríguez, Javier DeFelipe. Neuroinformatics, January 2014. Musical representation of dendritic spine distribution: a new exploratory tool

Neuronize

This tool presents a new technique for the generation of three-dimensional models for neuronal cells from the morphological information extracted through computed-aided tracing applications. The 3D polygonal meshes that approximate the cell membrane can be generated at different resolution levels, allowing balance to be reached between the complexity and the quality of the final model.

Neuronize implements a novel approach to generate a realistic 3D shape of the soma from the incomplete information stored in the digitally traced neuron using a physical deformation technique.

The addition of a set of spines along the dendrites completes the model, generating a final 3D neuronal cell suitable for its visualization in a wide range of 3D environments.

Brito JP, Mata S, Bayona S, Pastor L, Defelipe J, Benavides-Piccione R (2013). A tool for building realistic neuronal cell morphologies. Front Neuroanat. 2013 Jun 3;7:15. doi: 10.3389/fnana.2013.00015. eCollection 2013.

Neuronize requires Matlab Compiler Runtime 2012b (you can download freely, clicking on here) to be installed on your computer. Versions for Windows 32 and 64 bits platforms are available. Versions for Linux and Mac are under development.

The video may also help you see how to work with NEURONIZE. It shows a common working session:

SAS

Synaptic Apposition Surface (SAS)

We have developed an efficient computational technique to automatically extract the surface from synaptic junctions that have previously been three dimensionally reconstructed from actual tissue samples imaged by automated FIB/SEM.

This technique has been incorporated into EspINA and SAS structures can be computed automatically from reconstructed synapses.

Juan Morales, Angel Rodríguez, José-Rodrigo Rodríguez, Javier DeFelipe and Angel Merchán-Pérez (2013). Characterization and extraction of the synaptic apposition surface for synaptic geometry analysis Front. Neuroanat., 04 July 2013 | doi: 10.3389/fnana.2013.00020

Dissemination Material and Press Dossier (2009-2018)

December 2011. Alzheimer 3n.

December 2010. Cajal Blue Brain in Science.

June 2010. Cajal Blue Brain Project on media.

December 2009. First Year of th Project.

June 2009. The Launching of the project.

Press Dossier

Videos

In Collaboration with

Publications (2009-2018)

2009 Scientific Publications & Other Publications

2009 CBBP Scientific Publications

Merchán-Pérez A, Rodriguez JR, Ribak CE, DeFelipe J (2009) Proximity of excitatory and inhibitory axon terminals adjacent to pyramidal cell bodies provides a putative basis for nonsynaptic interactions. Proc Natl Acad Sci USA. 106(24):9878-83.
Merchan-Pérez A, Rodriguez J, Alonso-Nanclares L, Schertel A and DeFelipe J (2009) Counting synapses using FIB/SEM microscopy: a true revolution for ultrastructural volume reconstruction. Front. Neuroanat. 3:18. doi:10.3389/neuro.05.018.2009.
Inda MC, DeFelipe J, Munoz A. (2009) Morphology and Distribution of Chandelier Cell Axon Terminals in the Mouse Cerebral Cortex and Claustroamygdaloid Complex. Cereb Cortex. 19(1):41-54.
Knafo S, Alonso-Nanclares L, J. Gonzalez-Soriano J, Merino-Serrais P, I. Fernaud-Espinosa1 I, Ferrer I, DeFelipe J (2009) Widespread changes in dendritic spines in a model of Alzheimer’s disease. Cereb Cortex 19(3):586-92.
Knafo S, Venero C, Merino-Serrais P, Fernaud-Espinosa I, Gonzalez-Soriano J, Ferrer I, Santpere G and DeFelipe J (2009) Morphological alterations to neurons of the amygdala and impaired fear conditioning in a transgenic mouse model of Alzheimer’s disease. J. Pathol. 219:41-51.
Kastanauskaite A, Alonso-Nanclares L, Blazquez-Llorca L, Pastor J, Sola RG, DeFelipe J (2009) Alterations of the microvascular network in sclerotic hippocampi from patients with epilepsy. J Neuropathol Exp Neurol. 68:939-950.
Yague JG, Azcoitia I, DeFelipe J, Garcia-Segura LM, Muñoz A (2009) Aromatase expression in the normal and epileptic human hippocampus. Brain Res. 2009 Oct 6. [Epub ahead of print].
Garcia-Marin V, Blazquez-Llorca L, Rodriguez JR, Boluda S, Muntane G, Ferrer I, DeFelipe J (2009) Diminished perisomatic GABAergic terminals on cortical neurons adjacent to amyloid plaques. Front Neuroanat. 3:28. Epub 2009 Nov 20.
Perea G, Araque A (2009) Glia modulates synaptic transmission. Brain Research Reviews (in press).
Araque A, Navarrete M (2009) Glial cells in neuronal network function. Philosophical Transactions of the Royal Society B (in press).
Perea G, Navarrete M, Araque A (2009) Tripartite synapses: astrocytes process and control synaptic information. Trends in Neurosciences 32: 421-431.
A. LaTorre, JM Peña, S. Muelas, AA. Freitas. Learning Hybridization Strategies in Evolutionary Algorithms. Intelligent Data Analysis 14 (3), 2010
S. Muelas, JM Peña, A. LaTorre. A New Initialization Procedure for the Estimation of Distribution Algorithms. Softcomputing (Accepted for publication)
G. Beslon, D. Parsons, JM Peña, C Rigotti, Y. Sanchez-Dehesa. From Digital Genetics to Knowledge Discovery: Perspectives in Genetic Network Understanding Intelligent Data Analysis (Accepted for Publication)
MS. Pérez, A. Sánchez, JH. Abawajy, V. Robles JM. Peña An agent architecture for managing data resources in a grid environment Future Generation Computer Systems 25, (7), 2009, 747-755
Y. Sanchez-Dehesa, D. Parsons, JM. Peña, G. Beslon. Modelling Evolution of Regulatory Networks in Artificial Bacteria. Mathematical Modelling of Natural Phenomena 3(2), 2008 :27-66
S. Gonzalez, L. Guerra, V. Robles, JM. Peña, F. Famili. CliDaPa: A New Approach to Combining Clinical Data with DNA Microarrays Intelligent Data Analysis (Accepted for Publication)
G. Beslon, D. Parsons, Y. Sanchez-Dehesa, JM Peña, C. Knibbe. Scaling Laws in Bacterial Genomes : A Side-Effect of Selection of Mutational Robustness?. Biosystems (Accepted for Publication).
M-L. Zhang, JM Peña, V Robles. Feature selection for multi-label naive Bayes classification. Information Science 179 (19), 2009: 3218-3229
R. Santana, C. Bielza, P. Larrañaga, J. A. Lozano, C. Echegoyen, A. Mendiburu, R. Armañanzas, S. Shakya. MATEDA: A Matlab package for the implementation and analysis of estimation of distribution algorithms. Journal of Statistical Software. Accepted for publication.
D. Otaegui, S. Baranzini, R. Armañanzas, B. Calvo, M. Muñoz-Culla, P. Khankhanian, I. Inza, J. A. Lozano, A. Asensio, T. Castillo-Triviño, J. Olsacoaga, A. López de Munain. Differential micro RNA expression in PBMC from multiple sclerosis patients. PLoS ONE, vol 4(7), e6309.
Inza, B. Calvo, R. Armañanzas, E. Bengoetxea, P. Larrañaga, J. A. Lozano. Machine learning: An indispensable tool in bioinformatics. In R. Matthiesen, editor, Bioinformatics Methods in Clinical Research. Humana Press

Other CBBP Publications

DeFelipe J, Jones EG. (2009) Neocortical Microcircuits. Handbook of Brain Microcircuits. (ed. Gordon Shepherd and Sten Grillner), Oxford University Press, New York (en prensa).
DeFelipe J (2009) Cajal y las mariposas del alma: plasticidad cerebral y procesos mentales. Ateneo (Madrid).
LaTorre, JM. Peña, V. Robles, S. Muelas, P. deMiguel. Supercomputer Scheduling with Combined Evolutionary Techniques. In the book Meta-heuristics for Scheduling: Distributed Computing Environments. Springer-Verlag ISBN 978-3-540-69260-7

2010 Scientific Publications & Other Publications

2010 Scientific Publications

From the Connectome to the Synaptome: An Epic Love Story. DeFelipe J. Science 26 November 2010: Vol. 330 no. 6008 pp. 1198-1201 DOI: 10.1126/science.1193378
GABAergic complex basket formations in the human neocortex. Blazquez-Llorca L, García-Marín V, DeFelipe J. J Comp Neurol. 2010 Dec 15;518 (24):4917-37.PMID: 21031559 [PubMed - in process] Related citations
Differential distribution of neurons in the gyral white matter of the human cerebral cortex. García-Marín V, Blazquez-Llorca L, Rodriguez JR, Gonzalez-Soriano J, DeFelipe J. J Comp Neurol. 2010 Dec 1;518(23):4740-59.PMID: 20963826 [PubMed - in process] Related citations
Layer-specific alterations to CA1 dendritic spines in a mouse model of Alzheimer's disease. Merino-Serrais P, Knafo S, Alonso-Nanclares L, Fernaud-Espinosa I, Defelipe J. Hippocampus. 2010 Sep 16. [Epub ahead of print]PMID: 20848609 [PubMed - as supplied by publisher]Related citations
Pericellular innervation of neurons expressing abnormally hyperphosphorylated tau in the hippocampal formation of Alzheimer's disease patients. Blazquez-Llorca L, Garcia-Marin V, Defelipe J. Front Neuroanat. 2010 Jun 24;4:20. PMID: 20631843 [PubMed - in process]Free PMC Article Free textRelated citations
Alterations of cortical pyramidal neurons in mice lacking high-affinity nicotinic receptors. Ballesteros-Yáñez I, Benavides-Piccione R, Bourgeois JP, Changeux JP, DeFelipe J. Proc Natl Acad Sci U S A. 2010 Jun 22;107(25):11567-72. Epub 2010 Jun 7.PMID: 20534523 [PubMed - indexed for MEDLINE] Related citations
Cortical white matter: beyond the pale remarks, main conclusions and discussion. Defelipe J, Fields RD, Hof PR, Höistad M, Kostovic I, Meyer G, Rockland KS.Front Neuroanat. 2010 Mar 24;4:4. No abstract available. PMID: 20428509 [PubMed - in process] Free PMC ArticleFree textRelated citations
Cortical GABAergic Neurons: Stretching it Remarks, Main Conclusions and Discussion. Clancy B, Defelipe J, Espinosa A, Fairén A, Jinno S, Kanold P, Luhmann HJ, Rockland KS, Tamamaki N, Yan XX. Front Neuroanat. 2010 Mar 2; 4:7. No abstract available. PMID: 20224807 [PubMed - in process]Free PMC ArticleFree textRelated citations
Aromatase expression in the normal and epileptic human hippocampus. Yague JG, Azcoitia I, DeFelipe J, Garcia-Segura LM, Muñoz A. Brain Res. 2010 Feb 22; 1315:41-52. Epub 2009 Oct 6.PMID: 19815003 [PubMed - indexed for MEDLINE] Related citations
Dyrk1A is a major player in the dendritic abnormalities of Down syndrome patients. Martinez de Lagrán M., Benavides-Piccione R, Ballesteros-Yañez I, Calvo M, Morales M, Fillat C, DeFelipe J, Ramakers G, and Dierssen M (2010) Journal of Neuroscience (submitted).
Pyramidal cells in prefrontal cortex of primates: marked specializations differences in neuronal structure among species Elston GN, Benavides-Piccione R, Elston A, Manger PR, DeFelipe J (2010) Frontier in Neuroscience (submitted).
Casein kinase 2 and microtubules control axon initial segment formation. Sánchez-Ponce D, Muñoz A and Garrido JJ. Moll and Cell Neurosci 2010 (In press).
Navarrete M, Araque A. Endocannabinoids potentiate synaptic transmission through stimulation of astrocytes. Neuron (2010) 68, 113-26.
Araque A, Navarrete M. Glial cells in neuronal network function. Philos Trans R Soc Lond B Biol Sci. (2010) 365, 2375-81.
Perea G, Araque A. GLIA modulates synaptic transmission. Brain Res Rev. (2010) 63, 93-102.
Guillaume Beslon, David P. Parsons, Jose M. Peña, Christophe Rigotti, Yolanda Sanchez-Dehesa: From digital genetics to knowledge discovery: Perspectives in genetic network understanding. Intell. Data Anal. 14(2): 173-191 (2010)
S. Lasserre, J. Hernando, S. Hill, F. Schürmann, P. de Miguel, G. Abou-Jaoudé, H. Markram, "A Neuron Membrane Mesh Representation for Visualization of Electrophysiological Simulations", IEEE Transactions on Visualization and Computer Graphics, accepted on December 13th 2010
L Guerra, LM McGarry, V Robles, C Bielza, P Larrañaga, R Yuste, “Comparison between supervised and unsupervised classifications of neuronal cell types: a case study”, Developmental Neurobiology, vol 71 (1), pp 71-82, 2011
Santana, R., Bielza, C., Larrañaga, P. (2010) Optimizing Brain Networks Topologies Using Multi-objective Evolutionary Computation, Neurinformatics, in press, DOI 10.1007/s12021-010-9085-7
López-Cruz, P., Bielza, C., Larrañaga, P., Benavides-Piccione, R., DeFelipe, J. (2010) Models and Simulation of 3D Neuronal Dendritic Trees Using Bayesian Networks, Neuroinformatics, in press.

Other CBBP Publications

“El cerebro, la gran cepa azul”. Arte y neurociencia. Coordinador: Javier de Felipe. Noviembre 2010
DeFelipe J, Jones EG. (2010) Neocortical Microcircuits. Handbook of Brain Microcircuits. (ed. Gordon Shepherd and Sten Grillner), Oxford University Press, New York.
DeFelipe J, (2010) The birth of modern neuroscience. In: Portraits of the Mind: Visualizing the Brain from Antiquity to the 21st Century. Schoonover CE (ed). Abrams, New York.
DeFelipe J (2010) Reflexiones sobre lo bello, el arte y la evolución del cerebro. En Neuroestética. Martín Araguz A (ed) Madrid. Saned, pp 51-76.
DeFelipe J (2010) Sobre lo bello, el arte y la ciencia: Paisajes neuonales.Coordinadores Javier de Felipe y Franck González. En: El cerebro, la gran cepa azul. Arte y Neurociencia. Museo Elder. Las Palmas de Gran Canaria, pp 33-72.
Morales, D.A. Matemáticas aplicadas en la predicción en medicina. Un paseo por la Geometría. D. Morales. Departamento de Matemáticas de la Facultad de Ciencia y Tecnología. Universidad del País Vasco. 2010. Pp 149-167

Other Publications

Higuera-Matas, A., Montoya, G.L., Coria, S.M., Miguéns, M., García-Lecumberri, C., and Ambrosio, E (2010). Differential gene expression in the nucleus accumbens and frontal cortex of Lewis and Fischer 344 rats relevant to drug addiction. Current Neuropharmacolgy (En prensa).
Higuera-Matas A, Soto-Montenegro ML, Montoya GL, García-Vázquez V, Pascau J, Miguens M, Del Olmo N, Vaquero JJ, García-Lecumberri C, Desco M, Ambrosio E (2010). Chronic Cannabinoid Administration to Periadolescent Rats Modulates the Metabolic Response to Acute Cocaine in the Adult Brain. Mol Imaging Biol (Epub ahead of print).
Higuera-Matas A, Botreau F, Del Olmo N, Miguéns M, Olías O, Montoya GL, García-Lecumberri C, Ambrosio E (2010). Periadolescent exposure to cannabinoids alters the striatal and hippocampal dopaminergic system in the adult rat brain. Eur Neuropsychopharmacol. Dec; 20(12):895-906.
García-Lecumberri, C., Torres, I., Martín, S., Crespo, J.A., Miguéns, M., Higuera-Matas, A., Nicanor, C. Ambrosio, E (2010). Strain differences in the dose-response relationship for morphine self-administration and impulsive choice between lewis and fischer 344 rats. J. Psychopharmacology (Epub ahead of print).
Guillaume Beslon, David P. Parsons, Yolanda Sanchez-Dehesa, José M. Peña, Carole Knibbe: Scaling laws in bacterial genomes: A side-effect of selection of mutational robustness? Biosystems 102(1): 32-40 (2010)
Santiago González, Luis Guerra, Víctor Robles, José M. Peña, Fazel Famili: CliDaPa: A new approach to combining clinical data with DNA microarrays. Intell. Data Anal. 14(2): 207-223 (2010)
Antonio LaTorre, José María Peña, Santiago Muelas, Alex A. Freitas: Learning hybridization strategies in evolutionary algorithms. Intell. Data Anal. 14(3): 333-354 (2010)
S. Muelas, J.M. Peña, A. LaTorre, and V. Robles, “A new initialization procedure for the distributed estimation of distribution algorithms” Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2010.
S. Muelas, A. LaTorre, and J.M. Peña, “A New Methodology for the Automatic Creation of Adaptive Hybrid Algorithms” Intelligent Data Analysis Journal (accepted, to be published in 2011)
LaTorre, A.; Muelas, S. & Peña, J.M. A MOS-based Dynamic Memetic Differential Evolution Algorithm for Continuous Optimization: A Scalability Test (accessible on-line: DOI: 10.1007/s00500-010-0646-3) Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2010, 1-13
Vidaurre, D., Bielza, C., Larrañaga, P. (2010) Learning an L1-regularized Gaussian Bayesian Network in the Equivalence Class Space, IEEE Transactions on Systems, Man and Cybernetics, Part B, 40, 5, 1231–1242
Jorge Muruzábal · Diego Vidaurre · Julián Sánchez “SOMwise Regression: a new clusterwise regression method”, Neural Computing and Applications, (Submmitted in September 2010), 2011. In press.
Santana, R., Bielza, C., Larrañaga, P., Lozano, J.A., Echegoyen, C., Mendiburu, A., Armañanzas, A., Shakya, S. (2010) Mateda-2.0: Estimation of Distribution Algorithms in MATLAB, Journal of Statistical Software, 35, 7, 1-30
Cuesta, I., Bielza, C., Cuenca-Estrella, M., Larrañaga, P., Rodríguez-Tudela, J.L. (2010) Evaluation by data mining techniques of fluconazole breakpoints established by the clinical and laboratory standards institute (CLSI) and comparison with those of the European committee on antimicrobial susceptibility testing (EUCAST). Antimicrobial Agents and Chemotherapy, 54, 4, 1541-1546
Bielza, C., Gómez, M., Shenoy, P.P. (2010) Modelling Challenges with Influence Diagrams: Constructing Probability and Utility Models, Decision Support Systems, 49, 354–364
Bielza, C., Gómez, M., Shenoy, P.P. (2010) A Review of Representation Issues and Modelling Challenges with Influence Diagrams, Omega, 39, 227–241
C. Bielza, J. A. Fernández del Pozo, P. Larrañaga, E. Bengoetxea (2010) Multidimensional statistical analysis of the parameterization of a genetic algorithm for the optimal ordering of tables. Expert Systems with Applications (37), 804-815
R. Santana, P. Larrañaga, J. A. Lozano (2010) Learning factorizations in estimation of distribution algorithms using afinity propagation. Evolutionary Computation 18, 4, 515-546
Armañanzas, R., Saeys, Y., Inza, I., García-Torres, M., Bielza, C., van de Peer, Y., Larrañaga, P. (2011) Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms, IEEE/ACM Transactions on Computational Biology and Bioinformatics, in press, http://doi.ieeecomputersociety.org/10.1109/TCBB.2010.18 Borchani, H., Larrañaga, P., Bielza, C. (2010) Classifying Evolving Data Streams with Partially Labelled Data, Intelligent Data Analysis, in press
P. Larrañaga, S. Moral (2011) Probabilistic graphical models in artificial intelligence. Applied Soft Computing (in press)
Bielza, C., Li, G., Larrañaga, P. (2011). Multi-Dimensional Classfication with Bayesian Networks. International Journal of Approximate Reasoning (in press)
Bielza, C., Robles, V., Larrañaga, P. (2010) Regularized Logistic Regression without a Penalty Term: An Application to Cancer Classification with Microarray Data, Expert Systems with Applications, in press (doi: 10.1016/j.eswa.2010.09.140)
Fernández del Pozo, J.A., Bielza, C. (2011) Dealing with Complex Queries in Decision Support Systems, Data & Knowledge Engineering, 70, 167-181
D.A. Morales, E. Bengoetxea, P. Larranaga. Automatic characterization of human embryos for improved selection on in-vitro fertilization using Bayesian classifiers. Artificial Intelligence in Medicine. Submitted response for reviewers.R. Santana, A. Mendiburu, N. Zaitlen, E. Eskin and J. A. Lozano (2010). Multi-marker tagging SNP selection using estimation of distribution algorithms. Artificial Intelligence in Medicine. Vol. 50 No. 3. Pp. 193:201.
M. García-Torres, R. Armañanzas, C. Bielza, P. Larrañaga (2010). Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data. Information Sciences, accepted for publication.
A high performance suite of data services for grids. Alberto Sánchez, María S. Pérez, Jesús Montes, Toni Cortés. Future Generation Computer Systems, Elsevier Science Publishers ISSN:0167-739X , Volume 26, Number 4, page 622--632 – 2010
Finding order in chaos: a behavior model of the whole grid. Jesús Montes, Alberto Sánchez, Julio J. Valdés, María S. Pérez, Pilar Herrero. Concurrency and Computation: Practice and Experience. John Wiley & Sons, Volume 22, page 1386--1415 – 2010.
GCViR: Grid Content-Based Video Retrieval with Work Allocation Brokering. Pablo Toharia, Alberto Sánchez, José L. Bosque, Óscar D. Robles. Concurrency and Computation, Practice and Experience. John Wiley & Sons, Volume 22, page 1450--1475 – 2010
Toharia Pablo, Sánchez Campos Alberto, Bosque José L., Robles Sánchez Óscar D. “GCViR: Grid Content-Based Video Retrieval with Work Allocation Brokering”. Concurrency and Computation, Practice and Experience. John Wiley & Sons, Volume 22, page 1450--1475 – 2010
García Lorenzo Marcos, Otaduy Miguel Ángel, O ‘Sullivan Carol. Perceptually validated global/local deformations. Computer Animation and Virtual Worlds. Volume 21, Issue 3-4, pages 245–254, May 2010.
C. Pedraza, E. Castillo, J. Castillo, J. L. Bosque, J. I. Martínez, O. D. Robles, J. Cano, P. Huerta. Content-based image retrieval algorithm acceleration in a low-cost reconfigurable FPGA cluster. Vol: 56 (1), pp: 633-640, 2010, ISSN:1383-7621 DOI: 10.1016/j.sysarc.2010.07.017, Elsevier - Academic Press. Journal of Systems Architecture.
Bengoetxea, E., Larrañaga, P., Bielza, C., Fernández del Pozo, J.A. (2010) Optimal Row and Column Ordering to Improve Table Interpretation Using Estimation of Distribution Algorithms, Journal of Heuristics, DOI 10.1007/s10732-010-9145-z
C. Echegoyen, A. Mendiburu, R. Santana and J. A. Lozano. Towards understanding EDAs based on Bayesian networks through a quantitative analysis. IEEE Transactions on Evolutionary Computation. Accepted for publication.
C. Echegoyen, A. Mendiburu, R. Santana, and J. A. Lozano. Analyzing the k Most Probable Solutions in EDAs based on Bayesian Networks. In Exploitation of linkage learning in evolutionary algorithms. Evolutionary Learning and Optimization. Springer. Y.-P. Chen editor. 2010.

2011 Scientific Publications & Other Publications

2011 CBBP Scientific Publications

From the Connectome to the Synaptome: An Epic Love Story. DeFelipe J. Science 26 November 2010: Vol. 330 no. 6008 pp. 1198-1201 DOI: 10.1126/science.1193378
Elston G, Benavides-Piccione R, Elston A, Manger P and Defelipe J (2011). Pyramidal cells in prefrontal cortex: comparative observations reveal unparalleled specializations in neuronal structure among primate species. Front. Neuroanat. 5:2. doi: 10.3389/fnana.2011.00002
López, PL, Bielza C, Larrañaga P, Benavides-Piccione R, DeFelipe J (2011) Models and simulation of 3D neuronal dendritic trees using Bayesian networks. Neuroinformatics (in press).
Alonso-Nanclares L, Kastanauskaite A, Rodriguez JR, Gonzalez-Soriano J, DeFelipe J (2011) A stereological study of synapse number in the epileptic human hippocampus. Front. Neuroanat. 5:8. doi: 10.3389/fnana.2011.00008
DeFelipe J (2011) The evolution of the brain, the human nature of cortical circuits and intellectual creativity. Front. Neuroanat. 5:29.
Blazquez-Llorca L, Garcia-Marin V, Merino-Serrais P, Avila J and DeFelipe J (2011) Abnormal tau phosphorylation in the thorny excrescences of CA3 hippocampal neurons in patients with Alzheimer’s. J Alzheimer Disease 26: 683-698.
Sánchez-Ponce D, Blazquez-Llorca L, DeFelipe J, Garrido JJ, Muñoz A (2011) Co-localization of α-actinin and synaptopodin in the pyramidal cell axon initial segment. Cereb Cortex (in press).
S. Knafo, C. Venero, C. Sánchez-Puelles, I. Pereda-Peréz, A. Franco, C. Sandi, L.M. Suárez, J.M. Solís, L. Alonso-Nanclares, E.D. Martín, P. Merino-Serrais, E. Borcel, S. Li, Y. Chen, J. Gonzalez-Soriano , V. Berezin, E. Bock, J. De Felipe, J.A. Esteban. Facilitation of AMPA receptor synaptic delivery as a molecular mechanism for cognitive enhancement. PLoS Biology (in press).
Mara et al Dyrk1A influences neuronal morphogenesis through regulation of cytoskeletal dynamics in mammalian cortical neuron. Cerebr Corteex (in press).
Franco A, Knafo S, Banon-Rodriguez I, Merino-Serrais P, Fernaud-Espinosa I, Nieto M, Garrido JJ, Esteban JA, Wandosell F, Anton IM. (2011) WIP Is a Negative Regulator of Neuronal Maturation and Synaptic Activity. Cereb Cortex. Aug 1. [Epub ahead of print].
Merino-Serrais P, Knafo S, Alonso-Nanclares L, Fernaud-Espinosa I, Defelipe J (2011). Layer-specific alterations to CA1 dendritic spines in a mouse model of Alzheimer's disease. Hippocampus 21(10):1037-1044.
Sánchez-Ponce D, Muñoz A, Garrido JJ (2011) Casein kinase 2 and microtubules control axon initial segment formation. Moll and Cell Neurosci 46: 222- 234
Sánchez-Ponce D, DeFelipe J, Garrido JJ, AMuñoz A (2011) Maturation of the cisternal organelle in the hippocampal neuron’s axon initial segment. Moll and Cell Neurosci 48: 104-116
Sánchez-Ponce D, Blázquez Llorca L, DeFelipe J, Garrido JJ, Alberto Muñoz (2011) Colocalization of α-actinin and synaptopodin in the pyramidal cell axon initial segment. Cerebral Cortex Epub Sep 21
León G, DeFelipe J, Muñoz A 2011 Effects of amyloid-beta plaque proximity on the axon initial segment of pyramidal cells. Journal of Alzheimer’s Disease. In Press
J. Morales, L. Alonso-Nanclares, J. R. Rodríguez, A. Merchán-Pérez, J. de Felipe, A. Rodríguez. "Fast interactive quantification of synapses in the cerebral cortex” International Journal of Artificial Intelligence Tools. World Scientific Publishing Co. ISSN 0218-2130 DOI: 10.1142/S0218213011000139 Vol.: 20(2) Pages: 239-252 Apr., 2011
Navarrete M, Perea G, Fernandez de Sevilla D, Gómez-Gonzalo M, Nuñez A, Martín E and Araque A (2012) Astrocytes mediate in vivo cholinergic-induced synaptic plasticity. PLoS Biology 10: e1001259.
Navarrete M, Perea G, Maglio L, Pastor J, García de Sola R and Araque A (2011) Astrocyte-neuron signaling in human brain tissue. Cerebral Cortex, under revision.
Araque A, Navarrete M (2011). Electrically driven insulation in the central nervous system. Science 333: 1587-1588.
Navarrete M, Araque A (2011) Basal synaptic transmission: astrocytes rule! Cell 146: 675-677.
Porto-Pazos AB, Veiguela N, Mesejo P, Navarrete M, Alvarellos A, Ibáñez O, Pazos A, Araque A (2011) Artificial astrocytes improve neural network performance. PLoS One 6:e19109.
Guerra, L., McGarry, L., Robles, V., Bielza, C., Larrañaga, P., Yuste, R. (2011). Comparison between supervised and unsupervised classification of neuronal cell types: A case study. Developmental Neurobiology, 71 (1), 71-82.
R. Santana, C. Bielza, P. Larrañaga (2011). Optimizing brain networks topologies using multiobjective evolutionary computation. Neurinformatics, 9 (1), 3-19.
P. López-Cruz, C. Bielza, P. Larrañaga, R. Benavides-Piccione, J. DeFelipe (2011). Models and simulation of 3D neuronal dendritic trees using Bayesian networks. Neuroinformatics, 347-369.
D. Vidaurre, C. Bielza, P. Larrañaga (2011). On nonlinearity in neural encoding models applied to the primary visual cortex. Network: Computation in Neural Systems, 22 (1-4), 97-125.
D. Vidaurre, C. Bielza, P. Larrañaga (2011). Lazy lasso for local regression. Computational Statistics DOI: 10.1007/s00180-011-0274-0. In Press.
R. Armañanzas, P. Larrañaga, C. Bielza (2011). Ensemble transcript interaction networks: A case study on Alzheimer’s disease. Computer Methods and Programs in Biomedicine. In Press.
D. Vidaurre, C. Bielza, P. Larrañaga (2011). Forward stagewise naive Bayes. Progress in Artificial Intelligence. In Press. (with application to fMRI data analysis).
Vidaurre, D., Bielza, C. & Larrañaga, P. (2011). An L1-regularized naive Bayes-inspired classifier for discarding redundant predictors. Submitted to International Journal of Pattern Recognition and Artificial Intelligence. (with application to fMRI data analysis).
J. Morales, L. Alonso-Nanclares, J. R. Rodríguez, A. Merchán-Pérez, J. de Felipe, A. Rodríguez. "Fast interactive quantification of synapses in the cerebral cortex” International Journal of Artificial Intelligence Tools. World Scientific Publishing Co. ISSN 0218-2130 DOI: 10.1142/S0218213011000139 Vol.: 20(2) Pages: 239-252 Apr., 2011
J. Morales, L. Alonso-Nanclares, J. R. Rodríguez, J. de Felipe, A. Rodríguez and A. Merchán-Pérez. "ESPINA: a tool for the automated segmentation and counting of synapses in large stacks of electron microscopy images” Frontiers in Neuroanatomy ISSN 1662-5129 DOI: 10.3389/fnana.2011.00018 Vol.: 5(18) Pages: 1-8 Date: Mar., 2011.

Other Publications

Higuera-Matas, A., Montoya, G.L., Coria, S.M., MIGUÉNS, M., García-Lecumberri, C., and Ambrosio, E (2011). Differential gene expression in the nucleus accumbens and frontal cortex of Lewis and Fischer 344 rats relevant to drug addiction. Current Neuropharmacology 9(1):143-150.
Higuera-Matas A, Soto-Montenegro ML, Montoya GL, García-Vázquez V, Pascau J, MIGUÉNS M, Del Olmo N, Vaquero JJ, García-Lecumberri C, Desco M, Ambrosio E (2011). Chronic Cannabinoid Administration to Periadolescent Rats Modulates the Metabolic Response to Acute Cocaine in the Adult Brain. Mol Imaging Biol 13(3): 411-415.
Miguens M., Coria SM., Higuera-Matas A., Fole A., Ambrosio E., Del Olmo N. (2011). Depotentiation of hippocampal long-term potentiation depends on genetic background and is modulated by cocaine self-administration. Neuroscience 187: 36-42.
DeFelipe J and Sotelo C (2011) Goodbye Ted (an Obituary for Edward G. Jones). Front. Neuroanat. 5:44. doi: 10.3389/fnana.2011.00044
Alejandro Higuera-Matas, Miguel Miguens, Nuria del Olmo, Carmen García-Lecumberri and Emilio Ambrosio. Neural Changes Developed during the Extinction of Cocaine Self-Administration Behavior. Pharmaceuticals 2011, 4, 1315-1327; doi: 10.3390/ph4101315.
Armañanzas, R., Bielza, C., Larrañaga, P. & Martínez-Martín, P. (2011). Restating Parkinson's disease severity indices by means of non-motor criteria. Technical Report, UPM-FI/DIA/2011-2.

2012 Scientific Publications & Other Publications

2012 CBBP Scientific Publications

Benavides-Piccione R, Fernaud-Espinosa I, Robles V, Yuste R, DeFelipe J. Age-Based Comparison of Human Dendritic Spine Structure Using Complete Three-Dimensional Reconstructions. Cereb Cortex. 2012 Jun 17
Morales J, Benavides-Piccione R, Rodríguez A, Pastor L, Yuste R, DeFelipe J. Three-Dimensional Analysis of Spiny Dendrites Using Straightening and Unrolling Transforms. Neuroinformatics. 2012 May 27
Knafo S, Venero C, Sánchez-Puelles C, Pereda-Peréz I, Franco A, Sandi C, Suárez LM, Solís JM, Alonso-Nanclares L, Martín ED, Merino-Serrais P, Borcel E, Li S, Chen Y, Gonzalez-Soriano J, Berezin V, Bock E, DeFelipe J, Esteban JA. Facilitation of AMPA receptor synaptic delivery as a molecular mechanism for cognitive enhancement. PLoS Biol. 2012 Feb; 10 (2):e1001262. Epub 2012 Feb 21.
Martinez de Lagran M, Benavides-Piccione R, Ballesteros-Yañez I, Calvo M, Morales M, Fillat C, DeFelipe J, Ramakers GJ, Dierssen M. Dyrk1A Influences Neuronal Morphogenesis Through Regulation of Cytoskeletal Dynamics in Mammalian Cortical Neurons. Cereb Cortex. 2012 Jan 2.
DeFelipe J, Markram H, Rockland KS. The neocortical column. Front Neuroanat. 2012. 6: 22. Epub 2012 Jun 26
Avila J, León-Espinosa G, García E, García-Escudero V, Hernández F, DeFelipe J. Tau Phosphorylation by GSK3 in Different Conditions. Int J Alzheimers Dis. 2012; 2012:578373. Epub 2012 May 17.
León-Espinosa G, DeFelipe J, Muñoz A. Effects of amyloid-β plaque proximity on the axon initial segment of pyramidal cells. J Alzheimers Dis. 2012; 29 (4):841-52.
Rockland KS, DeFelipe J (2012) Cortical GABAergic neurons: stretching it. Front Neuroanat. 2012; 6:16. Epub 2012 May 31. Front Neuroanat. 2012; 6: 22.
Jimenez-Mateos EM, Engel T, Merino-Serrais P, McKiernan RC, Tanaka K, Mouri G, Sano T, O'Tuathaigh C, Waddington JL, Prenter S, Delanty N, Farrell MA, O'Brien DF, Conroy RM, Stallings RL, Defelipe J, Henshall DC (2012). Silencing microRNA-134 produces neuroprotective and prolonged seizure-suppressive effects. Nat Med. 18:1087-1094.
Sánchez-Ponce D, DeFelipe J, Garrido JJ, Muñoz A (2012) Developmental expression of Kv potassium channels at the axon initial segment of cultured hippocampal neurons. Plos One 7 (10):e48557. doi: 10.1371/journal.pone.0048557.
Merchán-Pérez A, Rodríguez J-R, González S, Robles V, DeFelipe J, Larrañaga P and Bielza C. Three-Dimensional Spatial Distribution of Synapses in the Neocortex: A Dual-Beam Electron Microscopy Study. Cerebral Cortex, in press.
Inan M, Blazquez-Llorca L, Merchan-Perez A, Anderson SA, DeFelipe J and Yuste R Dense and Overlapping Innervation of Pyramidal Neurons by Neocortical Chandelier Cells. Journal of Neuroscience, in press.
Llorens-Martín M, Fuster-Matanzo A, Teixeira CM, Jurado-Arjona J, Ulloa F, Defelipe J, Rábano A, Hernández F, Soriano E, Avila J. GSK-3β overexpression causes reversible alterations on postsynaptic densities and dendritic morphology of hippocampal granule neurons in vivo. Mol Psychiatry. doi: 10.1038/mp.2013.4. [Epub ahead of print]
Blazquez-Llorca L, Merchán-Pérez A, Rodríguez R, Gascón J, Defelipe J (2013) FIB/SEM Technology and Alzheimer's Disease: Three-Dimensional Analysis of Human Cortical Synapses. J Alzheimers Dis. [Epub ahead of print]
Leon-Espinosa G, Garcia E, Garcia-Escudero V, Hernandez F DeFelipe J, Avila,J Changes in tau phosphorylation in hibernating rodents. J Neurosci Res (in press)
Merino-Serrais P, Benavides-Piccione R, Blazquez-Llorca L, Kastanauskaite A, Rábano A, Avila J, DeFelipe J The influence of phosphotau on dendritic spines of cortical pyramidal neurons in Alzheimer’s disease patients. Brain (in press)
Alonso-Nanclares L, Merino-Serrais P, Gonzalez S, DeFelipe J. Synaptic changes in the dentate gyrus of app/ps1 transgenic mice revealed by electron microscopy. J Neuropathol Exp Neurol (in press)
Pedro L. López-Cruz, Pedro Larrañaga, Javier DeFelipe, Concha Bielza. Bayesian network modeling of the consensus between experts: An application to neuron classification. International Journal of Approximate Reasoning: accepted.
DeFelipe J, López-Cruz PL, Benavides-Piccione R, Bielza C, Larrañaga, et al. Classification and nomenclature of neocortical GABAergic interneurons. Nature Reviews Neuroscience: accepted.
Merchán-Pérez A, Rodríguez J-R, González S, Robles V, DeFelipe J, Larrañaga P and Bielza C. Three-Dimensional Spatial Distribution of Synapses in the Neocortex: A Dual-Beam Electron Microscopy Study. Cerebral Cortex, in press.
Larrañaga P, Concha Bielza C, DeFelipe J. Alan Turing y la neurociencia. Mente y Cerebro, 57, 49–51.
Santana, R., Bielza, C. & Larrañaga, P. Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models. BMC Neuroscience, 13(Suppl 1), P100.
Armañanzas, R., Larrañaga, P., Bielza, C. Ensemble transcript interaction networks: A case study on Alzheimer's disease. Computer Methods and Programs in Biomedicine, 108(1), 442-450.
Borchani, H., Bielza, C., Martínez-Martín, P., Larrañaga, P. Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39). Journal of Biomedical Informatics, 45, 1175-1184.
Morales, D., Vives-Gilabert, Y., Gómez-Ansón, B., Bengoetxea, E., Larrañaga, P., Bielza, C., Pagonabarraga, J., Kulisevsky, J., Corcuera-Solano, I., Delfino, M. Predicting dementia development in Parkinson's disease using Bayesian network classifiers. Psychiatry Research: NeuroImaging, accepted.
Santana, R., Bielza, C., Larrañaga, P. Regularized logistic regression and multi-objective variable selection for classifying MEG data. Biological Cybernetics, 106(6-7), 389-405.
Vidaurre, D., Bielza, C., Larrañaga, P. Lazy lasso for local regression. Computational Statistics, 27(3), 531-550 (with application to fMRI data analysis).
Vidaurre, D., Rodríguez, E.E., Bielza, C., Larrañaga, P., Rudomin, P. A new feature extraction method for signal classification applied to cat spinal cord signals. Journal of Neural Engineering, accepted.
Vidaurre, D., Bielza, C., Larrañaga, P. Classification of neural signals from sparse autoregressive features. Neurocomputing: accepted.
García-Bilbao, A., Armañanzas, R., Ispizua, Z., Calvo, B., Alonso-Varona, A., Inza, I., Larrañaga, P., López-Vivanco, G., Suárez-Merino, B., Betanzos, M. Identiﬁcation of a biomarker panel for colorectal cancer diagnosis. BMC Cancer, 12(43).
García-Torres, M., Armañanzas, R., Bielza, C. & Larrañaga, P. (2013). Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data. Information Sciences, 222, 229-246.
Borchani, H., Bielza, C., Larrañaga, P., Toro, C. Learning multi-dimensional Bayesian network classifiers using Markov blankets: A case study in the prediction of HIV-1 reverse transcriptase and protease inhibitors. Artificial Intelligence In Medicine, accepted.
Guerra, L., V. Robles, Bielza, C., Larrañaga, P. A comparison of clustering quality indices using outliers and noise. Intelligent Data Analysis, 16(4), 703-715.
Ibañez, A., Bielza, C., Larrañaga, P. Relationship among research collaboration, number of documents and number of citations. A case study in Spanish computer science production in 2000-2009. Scientometrics, accepted.
Ibañez, A., Bielza, C., Larrañaga, P. Analysis of scientific activity in Spanish public universities in the area of computer science. Revista Española de Documentación Científica, accepted.
Larrañaga P., Bielza, C. Alan Turing and Bayesian statistics. Mathware & Soft Computing Magazine, 19, 2, 23-24.
Larrañaga, P., Karshenas, H., Bielza, C., Santana, R. A review on probabilistic graphical models in evolutionary computation. Journal of Heuristics, 18(5), 795-819.
P. Larrañaga, H. Karshenas, C. Bielza, R. Santana. A Review on Evolutionary Algorithms in Bayesian Network Learning and Inference Tasks. Information Sciences, accepted.
H. Karshenas, R. Santana, C. Bielza, P. Larrañaga. Regularized Continuous Estimation of Distribution Algorithms. Applied Soft Computing, accepted.
Morales J, Benavides-Piccione R, Rodríguez A, Pastor L, Yuste R, DeFelipe J. “Three-dimensional analysis of spiny dendrites using straightening and unrolling transforms” Neuroinformatics Springer Verlag. Vol. 10 No. 4 ISSN 1539-2791 DOI: 10.1007/s12021-012-9153-2 pp. 391-407. 2012 Indexed: JCR
Merchán-Pérez A, Rodríguez J-R, González S, Robles V, DeFelipe J, Larrañaga P and Bielza C. Three-Dimensional Spatial Distribution of Synapses in the Neocortex: A Dual-Beam Electron Microscopy Study. Cerebral Cortex, in press.
García JA, Peña JM,. McHugh S, Jérusalem A. A model of the spatially dependent mechanical properties of the axon during its growth. Computational Modeling in Engineering & Science (CMES), in press.
Jérusalem A, Dao M. Continuum modeling of neuronal cell under blast loading. Acta Biomaterialia, 8 (9), 3360-3371, 2012
Navarrete M, Perea G, Maglio L, Pastor J, de Sola RG, Araque A (2012) Astrocyte calcium signal and gliotransmission in human brain tissue. Cerebral Cortex (in press)
Navarrete M, Perea G, Fernandez de Sevilla D, Gómez-Gonzalo M, Núñez A, Martín ED, Araque A (2012) Astrocytes mediate in vivo cholinergic-induced synaptic plasticity. Plos Biology 10: e1001259.
Zorec R, Araque A, Carmignoto G, Haydon PG, Verkhratsky A, Parpura V (2012) Astroglial excitability and gliotransmission: An appraisal of Ca2+ as a signaling route. ASN Neuro 4: e00080.

Other CBBP Publications

Karshenas, H., Santana, R., Bielza, C., Larrañaga, P. Continuous Estimation of Distribution Algorithms Based on Factorized Gaussian Markov Networks. In Adaptation, Learning & Optimization. Series Vol. 14 (editors), Markov Networks in Evolutionary Algorithms. Springer
Gonzalez S. Análisis de simulación de Sinapsis. Technical report.
Gracia A and S. Gonzalez S. MedVir aplicado a la morfología de las neuronas. Technical report.
Alonso-Nanclares L, Merino-Serrais P, Gonzalez S, DeFelipe J. Synaptic changes in the dentate gyrus of app/ps1 transgenic mice revealed by electron microscopy. J Neuropathol Exp Neurol (in press)

Other Publications

Peña JM. El Futuro de la Fármaco-Economía: Minería de datos y supercomputación, in “Fármaco-Economía” Monográficas de Oncología Médica, to appear 2012

2013 Scientific Publications & Other Publications

2013 CBBP Scientific Publications

Benavides-Piccione R, Fernaud-Espinosa I, Robles V, Yuste R, DeFelipe J. Age-Based Comparison of Human Dendritic Spine Structure Using Complete Three-Dimensional Reconstructions. Cereb Cortex. 2012 Jun 17
Armañanzas R, Alonso-Nanclares L, DeFelipe-Oroquieta J, Kastanauskaite A, de Sola R G, DeFelipe J, Bielza C, Larrañaga P (2013). Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery. PLoS ONE, 8 (4), e62819. PMID: 23646148
Alonso-Nanclares L, Merino-Serrais P, Gonzalez S, DeFelipe J (2013). Synaptic changes in the dentate gyrus of APP/PS1 transgenic mice reveales by electron microscopy. Journal of Neuropathology and Experimental Neurology 72(5): 386-95. Doi: 10.1097/ NEN.0b013e31828d41ec. PMID: 23584198.
LaTorre A, Alonso-Nanclares L, Muelas, S, Peña JM, DeFelipe J (2013). Segmentation of Neuronal Nuclei Based on Clump Splitting and a Two-step Binarization of Images. Expert Systems with Applications, 40 (16): 6521-30.
Alonso-Nanclares L and DeFelipe J (2013). Alterations of the microvascular network in the sclerotic hippocampus of patients with temporal lobe epilepsy. Epilepsy and Behaviour (in press)
LaTorre A, Alonso-Nanclares L, Muelas, S, Peña JM, DeFelipe J (2013). 3D Segmentations of Neuronal Nuclei from Confocal Microscope Image Stacks. Front. Neuroanat. 7:49. doi:10.3389/fnana.2013.00049
Blazquez-Llorca, L., Merchán-Pérez, A., Rodríguez, J.-R., Gascón, J., and Defelipe, J. (2013). FIB/SEM Technology and Alzheimer’s Disease: Three-Dimensional Analysis of Human Cortical Synapses. J. Alzheimers Dis. 34, 995–1013.
Inan, M., Blázquez-Llorca, L., Merchán-Pérez, A., Anderson, S. A., Defelipe, J., and Yuste, R. (2013). Dense and overlapping innervation of pyramidal neurons by chandelier cells. J. Neurosci. 33, 1907–1914.
Merchán-Pérez, A., Rodríguez, J.-R., González, S., Robles, V., DeFelipe, J., Larrañaga, P., and Bielza, C. (2013). Three-Dimensional Spatial Distribution of Synapses in the Neocortex: A Dual-Beam Electron Microscopy Study. Cereb. Cortex, bht018.
Montes, J., Gomez, E., Merchán-Pérez, A., DeFelipe, J., and Peña, J.-M. (2013). A Machine Learning Method for the Prediction of Receptor Activation in the Simulation of Synapses. PLoS ONE 8, e68888.
Morales, J., Rodríguez, A., Rodríguez, J.-R., DeFelipe, J., and Merchán-Pérez, A. (2013). Characterization and extraction of the synaptic apposition surface for synaptic geometry analysis. Front. Neuroanat 7, 20.
Benavides-Piccione R, Fernaud-Espinosa I, Robles V, Yuste R, DeFelipe J (2013) Age-Based Comparison of Human Dendritic Spine Structure Using Complete Three-Dimensional Reconstructions. Cereb Cortex 23:1798-1810.
DeFelipe J, López-Cruz PL, Benavides-Piccione R, Bielza C, Larrañaga P, Anderson S, Burkhalter A, Cauli B, Fairén A, Feldmeyer D, Fishell G, Fitzpatrick D, Freund TF, González-Burgos G, Hestrin S, Hill S, Hof PR, Huang J, Jones EG, Kawaguchi Y, Kisvárday Z, Kubota Y, Lewis DA, Marín O, Markram H, McBain CJ, Meyer HS, Monyer H, Nelson SB, Rockland K, Rossier J, Rubenstein JL, Rudy B, Scanziani M, Shepherd GM, Sherwood CC, Staiger JF, Tamás G, Thomson A, Wang Y, Yuste R, Ascoli GA (2013). New insights into the classification and nomenclature of cortical GABAergic interneurons. Nature Neuroscience 14:202-216.
Llorens-Martín M, Fuster-Matanzo A, Teixeira CM, Jurado-Arjona J, Ulloa F, Defelipe J, Rábano A, Hernández F, Soriano E, Avila J. GSK-3β overexpression causes reversible alterations on postsynaptic densities and dendritic morphology of hippocampal granule neurons in vivo. Mol Psychiatry. 2013 Apr; 18(4):451-60.
Leon-Espinosa G, Garcia E, Garcia-Escudero V, Hernandez F DeFelipe J, Avila,J (2013) Changes in tau phosphorylation in hibernating rodents. J Neurosci Res. J Neurosci Res. 2013 Apr 19. doi: 10.1002/jnr.23220.
Miguéns M, Kastanauskaite A, Coria SM, Selvas A, Ballesteros-Yañez I, Defelipe J, Ambrosio E (2013) The Effects of Cocaine Self-Administration on Dendritic Spine Density in the Rat Hippocampus Are Dependent on Genetic Background. Cereb Cortex. 2013 Aug 21. [Epub ahead of print]
Brito JP, Mata S, Bayona S, Pastor L, Defelipe J, Benavides-Piccione R. (2013) Neuronize: a tool for building realistic neuronal cell morphologies. Front Neuroanat. 2013; 7:15. doi: 10.3389/fnana.2013.00015.
Merino-Serrais P, Benavides-Piccione R, Blazquez-Llorca L, Kastanauskaite A, Rábano A, Avila J, DeFelipe J (2013) The influence of phosphotau on dendritic spines of cortical pyramidal neurons in Alzheimer’s disease patients. Brain 136:1913-1928.
Llorens-Martín M, Fuster-Matanzo A, Teixeira CM, Jurado-Arjona J, Ulloa F, Defelipe J, Rábano A, Hernández F, Soriano E, Avila J. (2013) Alzheimer disease-like cellular phenotype of newborn granule neurons can be reversed in GSK-3Î²-overexpressing mice. Mol Psychiatry. 18(4):395. doi: 10.1038/mp.2013.27
López-Cruz, P.L., Larrañaga, P., DeFelipe, J., Bielza, C. (2014) Bayesian network modeling of the consensus between experts: An application to neuron classification. International Journal of Approximate Reasoning 55 (1): 3-22. In Press
Alejandro Antón-Fernández, Pablo Rubio-Garrido, Javier DeFelipe, and Alberto Muñoz (2013) Selective presence of a giant saccular organelle in the axon initial segment of a subpopulation of layer V pyramidal neurons. Brain Structure and Function. [Epub ahead of print].
P. Toharia, J. Morales, O. de Juan, I. Fernaud-Espinosa, A. Rodríguez, J. DeFelipe, Musical representation of dendritic spine distribution: a new exploratory tool, Neuroinformatics Springer Verlag. ISSN 1539-2791 DOI: 10.1007/s12021-013-9195-0 (in, press) pp. 13
R. Armañanzas, C. Bielza, K.R. Chaudhuri, P. Martínez-Martín, P. Larrañaga (2013). Unveiling relevant non-motor Parkinson’s disease severity symptoms using a machine learning approach. Artificial Intelligence in Medicine, 58(3), 195–202.
H. Borchani, C. Bielza, P. Martínez-Martín, P. Larrañaga (2013). Predicting EQ-5D from the Parkinson’s disease questionnaire using multi-dimensional Bayesian network classifiers. Biomedical Engineering: Applications, Basis and Communications, in press.
D. Morales, Y. Vives-Gilabert, B. Gómez-Ansón, E. Bengoetxea, P. Larrañaga, C. Bielza, J. Pagonabarraga, J. Kulisevsky, I. Corcuera-Solano, M. Delfino (2013). Predicting dementia development in Parkinson's disease using Bayesian network classifiers. Psychiatry Research: NeuroImaging, 213, 92-98
R. Santana, L. McGarry, C. Bielza, P. Larrañaga, R. Yuste (2013). Classification of neocortical interneurons using affinity propagation. Frontiers in Neural Circuits, in press.
D. Vidaurre, C. Bielza, P. Larrañaga (2013). Classification of neural signals from sparse autoregressive features. Neurocomputing, 111, 21–26.
C. Bielza, J.A. Fernández del Pozo, P. Larrañaga (2013). Parameter control of genetic algorithms by learning and simulation of Bayesian Networks. A case study for the optimal ordering of tables. Journal of Computer Science and Technology, 28(4), 720–731.
H. Borchani, C. Bielza, C. Toro, P. Larrañaga (2013). Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers. Artificial Intelligence in Medicine, 57(3), 219–229.
M. García-Torres, R. Armañanzas, C. Bielza, P. Larrañaga (2013). Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data. Information Sciences, 222, 229–246.
L. Guerra, C. Bielza, V. Robles, P. Larrañaga (2013). Semi-supervised projected model-based clustering. Data Mining and Knowledge Discovery, in press.
A. Ibáñez, C. Bielza, P. Larrañaga (2013). Análisis de la actividad científica de las universidades públicas españolas en el área de las tecnologías informáticas. Revista Española de Documentación Científica, 36(1), e002
A. Ibáñez, C. Bielza, P. Larrañaga (2013). Cost-sensitive selective naive Bayes classifiers for predicting the increase of the h-index for scientific journals. Neurocomputing, in press.
A. Ibáñez, P. Larrañaga, C. Bielza (2013). Cluster methods for assessing research performance: Exploring Spanish computer science. Scientometrics, in press.
A. Ibáñez, C. Bielza, P. Larrañaga (2013). Relationship among research collaboration, number of documents and number of citations. A case study in Spanish computer science production in 2000-2009. Scientometrics, in press.
H. Karshenas, R. Santana, C. Bielza, P. Larrañaga (2013). Multi-objective estimation of distribution algorithms based on joint modeling of objectives and variables. IEEE Transactions on Evolutionary Computation, in press.
H. Karshenas, R. Santana, C. Bielza, P. Larrañaga (2013). Regularized continuous estimation of distribution algorithms. Applied Soft Computing, 13(5), 2412-2432.
P. Larrañaga, H. Karshenas, C. Bielza, R. Santana (2013). A review on evolutionary algorithms in Bayesian network learning and inference tasks. Information Sciences, 233, 109–125.
P.L. López-Cruz, C. Bielza, P. Larrañaga (2013). Directional naive Bayes classifiers. Pattern Analysis and Applications, in press.
P.L. López-Cruz, C. Bielza, P. Larrañaga (2013). Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation. International Journal of Approximate Reasoning, in press.
J. Read, C. Bielza, P. Larrañaga (2013). Multi-dimensional classification with super-classes. IEEE Transactions on Knowledge and Data Engineering, in press.
R. Santana, R. Armañanzas, C. Bielza, P. Larrañaga (2013). Network measures for information extraction in evolutionary algorithms. International Journal of Computational Intelligence Systems, 6(6), 1163-1188.
E. Sucar, C. Bielza, E. F. Morales, P. Hernández-Leal, J. H. Zaragoza, P. Larrañaga (2013). Multi-label classification with Bayesian network-based chain classifiers. Pattern Recognition Letters, in press.
D. Vidaurre, C. Bielza, P. Larrañaga (2013). A survey of L1 regression. International Statistical Review, in press.
D. Vidaurre, C. Bielza, P. Larrañaga (2013). An L1-regularized naive Bayes-inspired classifier for discarding redundant predictors. International Journal on Artificial Intelligence Tools, 22(4), 1350019.
D. Vidaurre, M. van Gerven, C. Bielza, P. Larrañaga, T. Heskes (2013). Bayesian partial least squares. Neural Computation, in press.
D. Vidaurre, C. Bielza, P. Larrañaga (2013). Sparse regularized local regression. Computational Statistics and Data Analysis, 62, 122–135.
Pablo Márquez-Neila, Luis Baumela, Luis Alvarez. A morphological approach to curvature-based evolution of curves and surfaces, IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 36(1), pp. 2-17, 2014.
A. Jérusalem, J.A. García, A. Merchán-Pérez and J.M. Peña, A computational model coupling mechanics and electrophysiology in spinal cord injury Biomechanics and Modeling in Mechanobiology, (in press)
Araque A, Camignoto G, Haydon PG, Oliet SH, Robitaille R, Volterra A (2014) Gliotransmitters travel in time and space. Neuron (in press).
Fields RD, Araque A, Johansen-Berg H, Lim SS, Lynch G, Nave KA, Nedergaard M, Perez R, Sejnowski T, Wake H (2013) Glial Biology in Learning and Cognition. The Neuroscientist (in press).
Pérez-Alvarez A, Araque A, Martín ED (2013) Confocal microscopy for astrocyte in vivo imaging: Recycle and reuse in microscopy. Frontiers in Cellular Neuroscience 7:51.
Pérez-Alvarez A, Araque A (2013) Astrocyte-neuron interaction at Tripartite Synapses. Current Drug Targets 14:1220-1224.
Navarrete M, Perea G, Maglio L, Pastor J, de Sola RG, Araque A (2013) Astrocyte calcium signal and gliotransmission in human brain tissue. Cerebral Cortex 23: 1240-1246.

Other CBBP Publications

Comment by Javier DeFelipe on: Stem Cells Treat Epileptic Symptoms in Mice. Alzheimer Research Forum. May 8, 2013. Available at http://www.alzforum.org/new/detail.asp?id=3483. Accessed May 17, 2013
DeFelipe J. Análisis del cerebro: innovación tecnológica y estrategia interdisciplinar. CIC NETWORK 24-28, 2013.
DeFelipe J. Cajal's Butterflies of the Soul. OUPblog as it continues to be a popular title at SFN.
DeFelipe J, Alonso-Nanclares L (2013). The Synapse: differences between men and women. In: D.W. Pfaff and Y. Christen (eds.), Multiple Origins of Sex Differences in Brain, Research and Perspectives in Endocrine Interactions. Springer-Verlag, Berlin Heidelberg (DOI 10.1007/978-3-642-33721-5_4)
DeFelipe J (2013) Cajal and the discovery of a new artistic world: The neuronal forest. Prog Brain Res. 2013; 203:201-220. In: The Fine Arts, Neurology and Neuroscience: History and Modem Perspectives. Eds: Stanley Finger, Dahlia Zajdel, Francois Boller and Julien Bogousslavsky. Elsevier, New York
C. Bielza, A. Salmerón, A. Alonso-Betanzos, J.I. Hidalgo, L. Martínez, A. Troncoso, E. Corchado, J.M. Corchado (Eds.) (2013). Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence, Vol 8109. Springer. 404 pages. ISBN: 978-3-642-40642-3.
C. Bielza, A. Salmerón (Eds.) (2013). Actas de la XV Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2013). 158 pages. ISBN: 978-84-695-8348-7.
C. Bielza, P. Larrañaga (2013). More than 20 entries in Concise Encyclopaedia of Bioinformatics and Computational Biology. Wiley.
L. Guerra, R. Benavides-Piccione, C. Bielza, V. Robles, J. DeFelipe, P. Larrañaga (2013). Semi-supervised projected clustering for classifying GABAergic interneurons. Artificial Intelligence in Medicine 2013, Lecture Notes in Artificial Intelligence 7885, pages 156-165, Springer.
P.L. Lopez-Cruz, C. Bielza, P. Larrañaga (2013). Learning conditional linear Gaussian classifiers with probabilistic class labels. Advances in Artificial Intelligence. Lecture Notes in Computer Science 8109, pages 139-148, Springer.
P.L. Lopez-Cruz, T.D. Nielsen, C. Bielza, P. Larrañaga (2013). Learning mixtures of polynomials of conditional densities from data. Advances in Artificial Intelligence. Lecture Notes in Computer Science 8109, pages 363-372, Springer.
B. Mihaljevic, P. Larrañaga, C. Bielza (2013). Augmented semi-naive Bayes classifier. Advances in Artificial Intelligence. Lecture Notes in Computer Science 8109, pages159-167, Springer.
R. Santana, C., Bielza, P. Larrañaga (2013). Changing conduction delays to maximize the number of polychronous groups with an estimation of distribution algorithm. Technical Report, UPM-FI/DIA/2013-1, Technical University of Madrid.13 pages.
Kendrick Cetina, Pablo Márquez-Neila, Luis Baumela. Un Estudio Comparativo de Descriptores de Caracteríısticas para la Segmentación de Sinapsis y Mitocondrias. En Actas del III Workshop de Reconocimiento de Formas y Análisis de Imágenes, pp. 25-28, Septiembre 2013.
Luis Baumela (Editor).Actas del III Workshop de Reconocimiento de Formas y Análisis de Imágenes. Madrid. 19 de septiembre de 2013. ISBN: 978-84-695-8332-6.
Araque A, Navarrete M (2013) El ayer y hoy de los astrocitos. Mente y Cerebro 60: 86-91.

Other Publications

P. Toharia, O. D. Robles, J. L. Bosque, Á. Rodríguez, Scalable shot boundary detection, Journal of Supercomputing. Springer Verlag. ISSN 0920-8542 DOI: 10.1007/s11227-012-0784-8 64(1): 89-99 Apr. 2013
Validation of a Virtual Reality Knee Arthroscopy Simulator for Surgical Training. Akhtar, K; Bayona, S.; Bello, F; Gupte, CM; Cobbs, JP; Arthroscopy: The Journal of Arthroscopic & Related Surgery. Volumen: Vol 29, 10 Pág. 138 -139. Fecha: 2013/10/31, 2013. Editorial: WB Saunders, Elsevier. ISSN: 07498063. doi: 10.1016/j.arthro.2013.07.177.
On-Board Multi-GPU Molecular Dynamics. Marcos Novalbos, Jaime Gonzalez, Miguel Angel Otaduy, Alvaro Lopez-Medrano, Alberto Sanchez: Euro-Par 2013: 862-873, August 2013
Assessing Skills Decay on a Knee Arthroscopy Simulator. Akhtar, K; Wijendra, A; Bayona,S.; Gupte,CM; Bello,F; Standfield, N.; Cobbs, JP; Arthroscopy: The Journal of Arthroscopic & Related Surgery. Volumen: Vol 29, 10 Pág. 139-139. Fecha: 2013/10/31, 2013 WB Saunders, Elsevier, ISSN: 07498063 doi: 10.1016/j.arthro.2013.07.177
GMonE: A complete approach to cloud monitoring. Jesús Montes, Alberto Sánchez, Bunjamin Memishi, Maria s. Pérez, Gabriel Antoniou. Future Generation Computer Systems. Volume 29, Issue 8, 2026—2040, October 2013
Jose L. Bosque, Pablo Toharia, Oscar D. Robles, Luis Pastor. "Impact of Information Exchange Policies in Load Balancing Algorithms". Journal of Supercomputing, under review.

2014 Scientific Publications & Other Publications

2014 CBBP Scientific Publications

Bielza C, Benavides-Piccione R, López-Cruz P, Larrañaga P, DeFelipe J (2014). Branching angles of pyramidal cell dendrites follow common geometrical design principles in different cortical areas. Sci. Rep. 4, 5909; DOI: 10.1038/srep05909.
Morales J*, Ruth Benavides-Piccione* R, Dar M, Fernaud I, Rodríguez A, Anton-Sanchez L, Bielza C, Larrañaga P, DeFelipe J, Yuste R (2014). Random Positioning of Dendritic Spines in the Human Cerebral Cortex. J Neurosci. 34:10078-10084. doi: 10.1523/JNEUROSCI.1085-14.2014. *both authors contributed equally
Anton-Sanchez L, Bielza C, Merchán-Pérez A, Rodríguez J-R, DeFelipe J and Larrañaga P (2014). Three-dimensional distribution of cortical synapses: a replicated point pattern-based analysis. Front.Neuroanat. 8:85. doi:10.3389/fnana.2014.00085. Trends Neurosci. 2014 Oct; 37(10):525-7. doi: 10.1016/j.tins.2014.08.002.
Mihaljević B, Bielza C, Benavides-Piccione R, DeFelipe J, Larrañaga P (2014) Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty. Front Comput Neurosci. 2014 Nov 25; 8:150. Doi: 10.3389/fncom.2014.00150.
Mihaljević B, Benavides-Piccione R, Bielza C, DeFelipe J, Larrañaga P (2014) Bayesian Network Classifiers for Categorizing Cortical GABAergic Interneurons. Neuroinformatics. 2014 Nov 25. [Epub ahead of print]
Jérusalem, A., García-Grajales, J.A., MerchánPérez, A., & Peña, J.M. (2014). A computational model coupling mechanics and electrophysiology in spinal cord injury. Biomechanics and Modeling in Mechanobiology (13):4 pp. 883-896
Pablo Toharia, Juan Morales, Octavio de Juan, Isabel Fernaud-Espinosa, Angel Rodríguez, Javier DeFelipe. “Musical representation of dendritic spine distribution: a new exploratory tool” Neuroinformatics Springer Verlag. ISSN 1539-2791 DOI: 10.1007/s12021-013-9195-0 12(2): 341-353 Apr. 2014
Loic Corenthy, Marcos García, Sofía Bayona Beriso, Andrea Santuy, Jose San Martin, Ruth Benavides-Piccione, Javier DeFelipe, Luis Pastor: A new user-adapted haptic Connection Procedure for the Reconstruction of Dendritic Spines. IEEE T. Haptics (TOH) 7(4):486-498 (2014)
Lopez-Cruz, P.L., Larrañaga, P., J. DeFelipe, & Bielza, C. (2014). Bayesian network modeling of the consensus between experts: An application to neuron classification. International Journal of Approximate Reasoning, 55(1), 3-22.
Merchán-Pérez, A., Rodríguez, R., González, S., Robles, V., DeFelipe, J., Larrañaga, P. & Bielza, C. (2014). Three-dimensional spatial distribution of synapses in the neocortex: A dual-beam electron microscopy study. Cerebral Cortex, 24, 1579-1588*.
Ferreira FR, Nogueira MI, DeFelipe J (2014). The influence of James and Darwin on Cajal and his research into the neuron theory and evolution of the nervous system. Front Neuroanat. 8:1. doi: 10.3389/fnana.2014.00001.
Mellström B, Sahún I, Ruiz-Nuño A, Murtra P, Gomez-Villafuertes R, Savignac M, Oliveros JC, Gonzalez P, Kastanauskaite A, Knafo S, Zhuo M, Higuera-Matas A, Errington ML, Maldonado R, DeFelipe J, Jefferys JG, Bliss TV, Dierssen M, Naranjo JR (2014) DREAM controls the on/off switch of specific activity-dependent transcription pathways. Mol Cell Biol 34:877-887.
Jimenez-Mateos EM, Engel T, Merino-Serrais P, Fernaud-Espinosa I, Rodriguez-Alvarez N, Reynolds J, Reschke CR, Conroy RM, McKiernan RC, DeFelipe J, Henshall DC. Antagomirs targeting microRNA-134 increase hippocampal pyramidal neuron spine volume in vivo and protect against pilocarpine-induced status epilepticus. Brain Struct Funct. 2014 May 30. [Epub ahead of print]
Llorens-Martín M, Blazquez-Llorca L, Benavides-Piccione R, Rabano A, Hernandez F, Avila J, DeFelipe J (2014). Selective alterations of neurons and circuits related to early memory loss in Alzheimer's disease. Front Neuroanat. 2014 May 27;8:38. doi: 10.3389/fnana.2014.00038.
DeFelipe J, Garrido E, Markram H (2014) The death of Cajal and the end of scientific romanticism and individualism. Trends Neurosci. 37(10): 525-527. doi: 10.1016/j.tins.2014.08.002.
Blazquez-Llorca L, Woodruff A, Inan M, Anderson SA, Yuste R, DeFelipe J, Merchan-Perez A. (2014) Spatial distribution of neurons innervated by chandelier cells. Brain Struct Funct. 2014 Jul 24. [Epub ahead of print]
Bielza, C. & Larrañaga, P. (2014b). Bayesian networks in neuroscience: A survey. Frontiers in Computational Neuroscience, 8, Article 131.
Borchani, H., Bielza, C., Martínez-Martín, P. & Larrañaga, P. (2014a). Predicting EQ-5D from the Parkinson’s disease questionnaire PDQ-8 using multi-dimensional Bayesian network classifiers. Biomedical Engineering: Applications, Basis and Communications, 26(1), 1450015.
Bielza, C. & Larrañaga, P. (2014c). Discrete Bayesian Network Classifiers: A Survey. ACM Computing Surveys, 47, 1, Article 5.
Guerra, L., Bielza, C., V. Robles & Larrañaga, P. (2014). Semi-supervised projected model-based clustering. Data Mining and Knowledge Discovery, 28(4), 882-917.
Ibañez, A., Bielza, C. & Larrañaga, P. (2014). Cost-sensitive selective naive Bayes classifiers for predicting the increase of the h-index for scientific journals. Neurocomputing, 135, 5, 42-52.
Karshenas, H., Santana, R., Bielza, C. & Larrañaga, P. (2014). Multi-objective estimation of distribution algorithm based on joint modeling of objectives and variables. IEEE Transactions on Evolutionary Computation, 18(4), 519-542.
Lopez-Cruz, P.L., Bielza, C. & Larrañaga, P. (2014). Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation. International Journal of Approximate Reasoning, 55(4), 989-1010.
Read, J., Bielza, C. & Larrañaga, P. (2014). Multi-dimensional classification with super-classes. IEEE Transactions on Knowledge and Data Engineering, 26, 7, 1720-1733.
Sucar, E., Bielza, C., Morales, E.F., Hernandez-Leal, P., Zaragoza, J.H. & Larrañaga, P. (2014). Multi-label classification with Bayesian network-based chain classifiers. Pattern Recognition Letters, 41, 14-22.
Antonio Gracia; Santiago Gonzalez; Víctor Robles; Ernestina Menasalvas. “A methodology to compare Dimensionality Reduction algorithms in terms of quality loss”. Information Sciences, 270(0): 1-27, DOI: 10.1016/j.ins.2014.02.068 Jun. 2014.
Antonio Gracia; Santiago González; Víctor Robles; Ernestina Menasalvas; Tatiana von Landesberger; “New insights into the suitability of the third dimension for visualizing multivariate/multidimensional data: A study based on loss of quality quantification”. Information Visualization. SAGE DOI: 10.1177/1473871614556393 Nov. 2014.
P. Marquez-Neila, L. Baumela, L. Alvarez. A Morphological Approach to Curvature-based Evolution of Curves and Surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36 (1):2-17, 2014.
Araque A, Camignoto G, Haydon PG, Oliet SH, Robitaille R, Volterra A (2014) Gliotransmitters travel in time and space. Neuron 81:728-739.
Perez-Alvarez A, Navarrete M, Covelo A, Martin ED, Araque A (2014) Structural and functional plasticity of astrocyte processes and dendritic spine interactions. Journal of Neuroscience 34:12738-12744.
Perea G, Sur M, Araque A (2014) Neuron-glia networks: integral gear of brain function. Frontiers in Cellular Neuroscience 8:378.
Jego P, Pacheco-Torres J, Araque A, Canals S (2014) Functional MRI in mice lacking IP3-dependent calcium signaling in astrocytes. Journal of Cerebral Blood Flow & Metabolism 34:1599-1603.
Navarrete M, Díez A, Araque A (2014) Astrocytes in endocannabinoid signaling. Philosophical Transactions of the Royal Society B 369: 20130599.
Navarrete M, Araque A (2014) The Cajal School and the physiological role of astrocytes: a way of thinking. Frontiers in Neuroanatomy 8:33.
Laura Raya, Sofía Bayona Beriso, Luis Pastor, Marcos García: A New User-Adapted Search Haptic Algorithm to Navigate along Filiform Structures. IEEE T. Haptics (TOH) 7(3):273-284 (2014)

Other CBBP Publications

DeFelipe J (2014) El Jardín de la Neurología: Sobre lo bello, el arte y el cerebro. Boletín Oficial del Estado y Consejo Superior de Investigaciones Científicas, Madrid.
G. Varando, C., Bielza, P. Larrañaga (2014). Multi-output regression models with MoPs Bayesian networks. Technical Report, UPM-FI/DIA/2014-2, Technical University of Madrid.15 pages.
Hernando, J., Duelo, C., Martin, V., Visualization of Large-Scale Neural Simulations. In: Brain-Inspired Computing. International Workshop, BrainComp 2013. Revised Selected Papers. L. Grandinetti et al. (eds.), Springer Verlag, LNCS 8603, pp. 184-197, 2014. Book chapter.
Muelas, S., Mendiburu, A., LaTorre, A., & Peña, J. M. (2014). Distributed Estimation of Distribution Algorithms for continuous optimization: How does the exchanged information influence their behavior? Information Sciences, 268, 231-254.
Gracia; A., Gonzalez; S., Robles; V., Menasalvas, E. (2014). A methodology to compare Dimensionality Reduction algorithms in terms of quality loss”. Information Sciences, 270(0): 1-27, DOI: 10.1016/j.ins.2014.02.068
Peña, J. M., Viedma, J., Muelas, S., LaTorre, A., & Peña, L. (2014, August). Designer-driven 3D buildings generated using Variable Neighborhood Search. In Computational Intelligence and Games (CIG), 2014 IEEE Conference on (pp. 1-8). IEEE.
Gracia, A., González, S., Robles, V., Menasalvas, E., & von Landesberger, T. (2014). New insights into the suitability of the third dimension for visualizing multivariate/multidimensional data: A study based on loss of quality quantification. Information Visualization, 1473871614556393.
LaTorre, A., Muelas, S., & Peña, J. M. (2014). A comprehensive comparison of large-scale global optimizers. Information Sciences doi:10.1016/j.ins.2014.09.031
Bayona, S; SanMartin, J; Miraut, D.; Gomez, C. VR-learning Surgical Simulators. Manipulability and Validation. Journal of Society for Development of Teaching and Business Processes in New Environment in B&H. Technics Technologies Educating Management 9(2). 305-316. 2014

2015 Scientific Publications & Other Publications

2015 CBBP Scientific Publications

Markram H, Muller E, Ramaswamy S, Reimann MW, Abdellah M, Sanchez CA, Ailamaki A, Alonso-Nanclares L, Antille N, Arsever S, Kahou GA, Berger TK, Bilgili A, Buncic N, Chalimourda A, Chindemi G, Courcol JD, Delalondre F, Delattre V, Druckmann S, Dumusc R, Dynes J, Eilemann S, Gal E, Gevaert ME, Ghobril JP, Gidon A, Graham JW, Gupta A, Haenel V, Hay E, Heinis T, Hernando JB, Hines M, Kanari L, Keller D, Kenyon J, Khazen G, Kim Y, King JG, Kisvarday Z, Kumbhar P, Lasserre S, Le Bé JV, Magalhães BR, Merchán-Pérez A, Meystre J, Morrice BR, Muller J, Muñoz-Céspedes A, Muralidhar S, Muthurasa K, Nachbaur D, Newton TH, Nolte M, Ovcharenko A, Palacios J, Pastor L, Perin R, Ranjan R, Riachi I, Rodríguez JR, Riquelme JL, Rössert C, Sfyrakis K, Shi Y, Shillcock JC, Silberberg G, Silva R, Tauheed F, Telefont M, Toledo-Rodriguez M, Tränkler T, Van Geit W, Díaz JV, Walker R, Wang Y, Zaninetta SM, DeFelipe J, Hill SL, Segev I, Schürmann F. (2015). Reconstruction and Simulation of Neocortical Microcircuitry. Cell 163: 1–37.
Ramaswamy S, Courcol JD, Abdellah M, Adaszewski SR, Antille N, Arsever S, Atenekeng G, Bilgili A, Brukau Y, Chalimourda A, Chindemi G, Delalondre F, Dumusc R, Eilemann S, Gevaert ME, Gleeson P, Graham JW, Hernando JB, Kanari L, Katkov Y, Keller D, King JG, Ranjan R, Reimann MW, Rössert C, Shi Y, Shillcock JC, Telefont M, Van Geit W, Villafranca Diaz J, Walker R, Wang Y, Zaninetta SM, DeFelipe J, Hill SL, Muller J, Segev I, Schürmann F, Muller EB, Markram H (2015). The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex. Front Neural Circuits. 9:44. doi: 10.3389/fncir.2015.00044
Montes J, Peña JM, DeFelipe J, Herreras O, Merchan-Perez A. The influence of synaptic size on AMPA receptor activation: a Monte Carlo model. PLoS One. 2015 Jun 24; 10(6):e0130924. doi: 10.1371/journal.pone.0130924. eCollection 2015.
Montes, J., LaTorre, A., Muelas, S., Merchán-Pérez, A., and Peña, J. M. (2015). Comparative Study of Metaheuristics for the Curve-Fitting Problem: Modeling Neurotransmitter Diffusion and Synaptic Receptor Activation. Abstract and Applied Analysis 2015, 16.
Mihaljevic B, Benavides-Piccione R, Bielza C , DeFelipe J, Larrañaga P (2015) Bayesian network classifiers for categorizing cortical GABAergic interneurons. Neuroinformatics 13:193-208.
Luengo-Sanchez S, Bielza C, Benavides-Piccione R, Fernaud-Espinosa I, DeFelipe J, Larrañaga P. (2015) A univocal definition of the neuronal soma morphology using Gaussian mixture models. Front Neuroanat. 9:137.
Rojo, C., I. Leguey, A. Kastanauskaite, C. Bielza, P. Larrañaga, J. DeFelipe, and R. Benavides-Piccione, Laminar differences in dendritic structure of pyramidal neurons in juvenile rat somatosensory cortex, Cerebral Cortex, accepted, 2016 (doi: 10.1093/cercor/bhv316).
Mihaljevic B, Guerra L, Benavides-Piccione R, DeFelipe J, Larrañaga P, Bielza C (2015) Classifying GABAergic interneurons with semi-supervised projected model-based clustering. Artif Intell Med. 65:49-59.
DeFelipe J (2015) The dendritic spine story: an intriguing process of discovery. Front Neuroanat. 2015 Mar 5;9:14. doi: 10.3389/fnana.2015.00014. eCollection 2015.
Íbias J, Soria-Molinillo E, Kastanauskaite A, Orgaz C, DeFelipe J, Pellón R, Miguéns M (2015). Schedule-induced polydipsia is associated with increased spine density in dorsolateral striatum neurons. Neuroscience. 2015 May 16;300:238-245. doi: 10.1016/j.neuroscience.2015.05.026.
Bosch C, Martínez A, Masachs N, Teixeira CM, Fernaud I, Ulloa F, Pérez-Martínez E, Lois C, Comella JX, DeFelipe J, Merchán-Pérez A, Soriano E (2015) FIB/SEM technology and high-throughput 3D reconstruction of dendritic spines and synapses in GFP-labeled adult-generated neurons. Front Neuroanat. 2015 May 21;9:60. doi: 10.3389/fnana.2015.00060. eCollection 2015.
Rábano A, Cuadros R, Merino-Serráis P, Rodal I, Benavides-Piccione R, Gómez E, Medina M, DeFelipe J, Avila J. Protocols for Monitoring the Development of Tau Pathology in Alzheimer's Disease. Methods Mol Biol. 2016, 1303:143-60. doi: 10.1007/978-1-4939-2627-5_7.
Selvas A, Coria SM, Kastanauskaite A, Fernaud-Espinosa I, DeFelipe J, Ambrosio E, Miguéns M (2015). Rat-strain dependent changes of dendritic and spine morphology in the hippocampus after cocaine self-administration. Addict Biol. 2015 Sep 2. doi: 10.1111/adb.12294.
DeFelipe J (2015). The anatomical problem posed by brain complexity and size: a potential solution. Front Neuroanat. 9:104. doi: 10.3389/fnana.2015.00104. eCollection 2015.
Chamorro-López J, Miguéns M, Morgado-Bernal I, Kastanauskaite A, Selvas A, Cabané-Cucurella A, Aldavert-Vera L, DeFelipe J, Segura-Torres P (2015). Structural plasticity in hippocampal cells related to the facilitative effect of intracranial self-stimulation on a spatial memory task. Behav Neurosci. 129:720-730.
Antón-Fernández A, León-Espinosa G, DeFelipe J, Muñoz A (2015). Changes in the Golgi Apparatus of Neocortical and Hippocampal Neurons in the Hibernating Hamster. Front Neuroanat. 9:157.
Benjumeda, M., Larrañaga, P., Bielza, C., Learning Bayesian networks with low inference complexity, Progress in Artificial Intelligence, accepted, 2015 (DOI 10.1007/s13748-015-0070-0).
Borchani, H., G. Varando, C. Bielza, and P. Larrañaga, Univariate and bivariate truncated regression, WIREs Data Mining and Knowledge Discovery, 5, 216-233, 2015.
Ibañez, A., R. Armañanzas, C. Bielza, and P. Larrañaga, Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices, Journal of the American Society for Information Science and Technology, accepted, 2015 (DOI: 10.1002/asi.23467).
Karshenas, H., C. Bielza, and P. Larrañaga, Interval-based ranking in noisy evolutionary multi-objective optimization, Computational Optimization and Applications, 61, 2, 517-555, 2015.
Larrañaga, A., C. Bielza, P. Pongrácz, T. Faragó, P. Bálint, and P. Larrañaga, Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking, Animal Cognition, 18, 2, 405-421, 2015.
Lopez-Cruz, P. L., C. Bielza, and P. Larrañaga, Directional naive Bayes classifiers, Pattern Analysis and Applications, 18, 225-246, 2015.
Varando, G., C. Bielza, and P. Larrañaga, Decision functions for chain classifiers based on Bayesian networks for multi-label classification, International Journal of Approximate Reasoning, 68, 164-178, 2016.
Varando, G., C. Bielza, and P. Larrañaga, Decision boundary for discrete Bayesian network classifiers, Journal of Machine Learning Research, 16, 2015.
Varando, G., P. L. Lopez-Cruz, T. D. Nielsen, P. Larrañaga, and C. Bielza, Conditional density approximations with mixtures of polynomials, International Journal of Intelligent Systems, 30, 3, 236–264, 2015.
García-Grajales JA, Rucabado G, García-Dopico A, Peña J-M, Jérusalem A (2015) Neurite, a Finite Difference Large Scale Parallel Program for the Simulation of Electrical Signal Propagation in Neurites under Mechanical Loading. PLoS ONE 10(2): e0116532. doi:10.1371/journal.pone.0116532.
Fernando Maestú, Jose-Maria Peña, Pilar Garcés, Santiago González, Ricardo Bajo, Anto Bagic, Pablo Cuesta, Michael Funke, Jyrki P. Mäkelä, Ernestina Menasalvas, Akinori Nakamura, Lauri Parkkonen, Maria E. López, Francisco del Pozo, Gustavo Sudre, Edward Zamrini, Eero Pekkonen, Richard N. Henson, James T. Becker, A multicenter study of the early detection of synaptic dysfunction in Mild Cognitive Impairment using Magnetoencephalography-derived functional connectivity, NeuroImage: Clinical, Volume 9, 2015, Pages 103-109, ISSN 2213-1582, http://dx.doi.org/10.1016/j.nicl.2015.07.011.
Siddiqui, Zaigham Faraz and Krempl, Georg and Spiliopoulou, Myra and Pena, Jose M and Paul, Nuria and Maestu, Fernando. Predicting the post-treatment recovery of patients suffering from traumatic brain injury (TBI), Brain Informatics, Volume 2, Number 1, pages 33--44, 2015.
Loic Corenthy, Miguel A. Otaduy, Luis Pastor, Marcos García. “Volume Haptics with Topology-Consistent Isosurfaces”. IEEE T. Haptics 8(4): 480-491 (2015)
Ángela Mendoza Mendoza, Susana Mata, Luis Pastor: “Non-Photorealistic Rendering of Neural Cells from their Morphological Description.” J. UCS 21(7): 935-958 (2015)
Martin R, Bajo-Grañeras R, Moratalla R, Perea G, Araque A (2015) Circuit-specific signaling in astrocyte-neuron networks in basal ganglia pathways. Science 349:730-734.
Oliveira JF, Sardinha VM, Guerra-Gomes S, Araque A, Sousa N (2015) Do stars govern our actions? Astrocyte involvement in rodent behavior. Trends in Neurosciences 38:535-549.
Gómez-Gonzalo M, Navarrete M, Perea G, Covelo A, Martín-Fernández M, Shigemoto R, Luján R, Araque A (2015) Endocannabinoids induce lateral long-term potentiation of transmitter release by stimulation of gliotransmission. Cerebral Cortex 25:3699-3712.

Other CBBP Publications

Cajal and de Castro´s Manual for Micrographic Technique. Editors: M.A. Merchán, J. DeFelipe and F. de Castro II. Oxford University Press: New York.
DeFelipe J (2015) Cajal y sus dibujos: cuando la ciencia era arte. Fisiología de los sueños. Cajal, Tanguy, Lorca, Dalí. Ed: Jaime Brihuega. Cultura y Política Social, Universidad de Zaragoza.
Anton-Sanchez, L., Bielza, C., Larrañaga, P. Evolutionary computation of forests with degree- and role-constrained minimum spanning trees. Technical Report TR:UPM-ESTIINF/DIA/ 2015-2, Universidad Politécnica de Madrid, 2015.
Fernandez-Gonzalez, P., Bielza, C., Larrañaga, P. Univariate and bivariate truncated vonMises distributions. Technical Report TR:UPM-ESTIINF/DIA/2015-1, Universidad Politécnica de Madrid, 2015.
Mihaljević, B., Bielza, C., Larrañaga P. Neuroclassifier: An automatic classification of cortical GABAergic interneurons. 2nd Human Brain Project Education Workshop: Future Medicine – Medical Intelligence for Brain Diseases. Centre Hospitalier Universitaire Vaudois.
Antonio LaTorre, Santiago Muelas, José-María Peña, A comprehensive comparison of large scale global optimizers, Information Sciences, Volume 316, 20 September 2015, Pages 517-549, ISSN 0020-0255, http://dx.doi.org/10.1016/j.ins.2014.09.031.
Santiago Muelas, Antonio LaTorre, José-María Peña, A distributed VNS algorithm for optimizing dial-a-ride problems in large-scale scenarios, Transportation Research Part C: Emerging Technologies, Volume 54, May 2015, Pages 110-130, ISSN 0968-090X, http://dx.doi.org/10.1016/j.trc.2015.02.024.
Antonio Gracia Berná, Santiago González, Víctor Robles, Ernestina Menasalvas Ruiz, Tatiana von Landesberger. New insights into the suitability of the third dimension for visualizing multivariate/multidimensional data: A study based on loss of quality quantification. Information Visualization 15(1): 3-30 (2016)

2016 Scientific Publications & Other Publications

2016 CBBP Scientific Publications

Anton-Sanchez, L., C. Bielza, P. Larrañaga, and J. DeFelipe, "Wiring Economy of Pyramidal Cells in the Juvenile Rat Somatosensory Cortex", PLoS ONE, vol. 11, issue 11, 2016.
Anton-Sanchez, L., C. Bielza, R. Benavides-Piccione, J. DeFelipe, and P. Larrañaga, "Dendritic and axonal wiring optimization of cortical GABAergic interneurons", Neuroinformatics, vol. 14, issue 4, pp. 453-464, 2016.
Leguey, I., C. Bielza, P. Larrañaga, A. Kastanauskaite, C. Rojo, R. Benavides-Piccione, and J. DeFelipe, "Dendritic branching angles of pyramidal cells across layers of the juvenile rat somatosensory cortex", Journal of Comparative Neurology, vol. 524, issue 13, pp. 2567-2576, 2016.
Rojo, C., I. Leguey, A. Kastanauskaite, C. Bielza, P. Larrañaga, J. DeFelipe, and R. Benavides-Piccione, "Laminar differences in dendritic structure of pyramidal neurons in juvenile rat somatosensory cortex", Cerebral Cortex, vol. 26, issue 6, pp. 2811-2822, 2016.
Márquez Neila, P. M., Baumela, L., González-Soriano, J., Rodríguez, J.-R., DeFelipe, J., and Merchán-Pérez, Á. (2016). A Fast Method for the Segmentation of Synaptic Junctions and Mitochondria in Serial Electron Microscopic Images of the Brain. Neuroinform 14, 235–250. doi:10.1007/s12021-015-9288-z.
Toharia P, Robles OD, Fernaud-Espinosa I, Makarova J, Galindo SE, Rodriguez A, Pastor L, Herreras O, DeFelipe J, Benavides-Piccione R (2016). PyramidalExplorer: A new interactive tool to explore morpho-functional relations of human pyramidal neurons. Front Neuroanat. 9:159.
Bosch, C., Masachs, N., Exposito-Alonso, D., Martínez, A., Teixeira, C. M., Fernaud, I., et al. (2016). Reelin Regulates the Maturation of Dendritic Spines, Synaptogenesis and Glial Ensheathment of Newborn Granule Cells. Cerebral Cortex. doi:10.1093/cercor/bhw216.
Eyal G, Verhoog MB, Testa-Silva G, Deitcher Y, Lodder JC, Benavides-Piccione R, Morales J, DeFelipe J, de Kock CP, Mansvelder HD, Segev I. Unique membrane properties and enhanced signal processing in human neocortical neurons. Elife. 2016 Oct 6;5. pii: e16553. doi: 10.7554/eLife.16553.
Mellström B, Kastanauskaite A, Knafo S, Gonzalez P, Dopazo XM, Ruiz-Nuño A, Jefferys JG, Zhuo M, Bliss TV, Naranjo JR, DeFelipe J (2016). Specific cytoarchitectureal changes in hippocampal subareas in daDREAM mice. Mol Brain. 2016 Feb 29;9:22. doi: 10.1186/s13041-016-0204-8.
Broadhead MJ, Horrocks MH, Zhu F, Muresan L, Benavides-Piccione R, DeFelipe J, Fricker D, Kopanitsa MV, Duncan RR, Klenerman D, Komiyama NH, Lee SF, Grant SG (2016) PSD95 nanoclusters are postsynaptic building blocks in hippocampus circuits. Sci Rep. 2016 Apr 25;6:24626. doi: 10.1038/srep24626.
DeFelipe J (2016). Phospho-Tau and Cognitive Decline in Alzheimer's Disease. Commentary: Tau in physiology and pathology. Front Neuroanat. 2016 Apr 28;10:44. doi: 10.3389/fnana.2016.00044.
DeFelipe J, Douglas RJ, Hill SL, Lein ES, Martin KA, Rockland KS, Segev I, Shepherd GM, Tamás G. (2016). Comments and General Discussion on "The Anatomical Problem Posed by Brain Complexity and Size: A Potential Solution". Front Neuroanat. 2016 Jun 10;10:60. doi: 10.3389/fnana.2016.00060.
León-Espinosa G, García E, Gómez-Pinedo U, Hernández F, DeFelipe J, Ávila J. (2016). Decreased adult neurogenesis in hibernating Syrian hamster. Neuroscience. 2016 Oct 1;333:181-92. doi: 10.1016/j.neuroscience.2016.07.016.
Barth A, Burkhalter A, Callaway EM, Connors BW, Cauli B, DeFelipe J, Feldmeyer D, Freund T, Kawaguchi Y, Kisvarday Z, Kubota Y, McBain C, Oberlaender M, Rossier J, Rudy B, Staiger JF, Somogyi P, Tamas G, Yuste R. (2016). Comment on "Principles of connectivity among morphologically defined cell types in adult neocortex". Science. 2016 Sep 9;353(6304):1108. doi: 10.1126/science.aaf5663.
Anton-Sanchez, L., C. Bielza, and P. Larrañaga, "Network Design through Forests with Degree- and Role-constrained Minimum Spanning Trees", in press, Journal of Heuristics
Benjumeda, M., C. Bielza, and P. Larrañaga, "Learning Bayesian networks with low inference complexity", Progress in Artificial Intelligence, vol. 5, issue 1, pp. 15-26, 2016.
Borchani, H., P. Larrañaga, J. Gama, and C. Bielza, "Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers", Intelligent Data Analysis, vol. 20, no. 2, 2016.
P. Fernandez-Gonzalez, P. Larrañaga, C. Bielza, “Univariate and bivariate truncated von Mises distributions", Progress in Artificial Intelligence, in press (doi:10.1007/s13748-016-0109-x)
Leitner, L., C. Bielza, S.L. Hill, and P. Larrañaga, "Data Publications Correlate with Citation Impact", Frontiers in Neuroscience, vol. 10, issue 419, 2016.
Rodriguez-Lujan, L., P. Larrañaga, and C. Bielza, "Frobenius norm regularization for the multivariate von Mises distribution", International Journal of Intelligent Systems, in press (DOI: 10.1002/int.21834).
Varando, G., C. Bielza, and P. Larrañaga, "Decision functions for chain classifiers based on Bayesian networks for multi-label classification", International Journal of Approximate Reasoning, vol. 68, pp. 164-178, 2016.
Galindo, S. E., Toharia, P., Robles, O. D., Pastor, L. (2016). ViSimpl: Multi-View Visual Analysis of Brain Simulation Data. Frontiers in Neuroinformatics, 10, 44. http://doi.org/10.3389/fninf.2016.00044
Herreras O. Local Field Potentials: Myths and Misunderstandings. Front Neural Circuits. 2016 Dec 15; 10:101. doi: 10.3389/fncir.2016.00101. eCollection 2016. Review.
Benito N, Martín-Vázquez G, Makarova J, Makarov VA, Herreras O. The right hippocampus leads the bilateral integration of gamma-parsed lateralized information. Elife. 2016 Sep 6; 5. pii: e16658. doi: 10.7554/eLife.16658.
Dreier JP, Fabricius M, Ayata C, …, Herreras O, et al. Recording, analysis, and interpretation of spreading depolarizations in neurointensive care: Review and recommendations of the COSBID research group. J Cereb Blood Flow Metab. 2016 Jan 1:271678X16654496. doi: 10.1177/0271678X16654496.
Martín-Vázquez G, Benito N, Makarov VA, Herreras O, Makarova J. Diversity of LFPs Activated in Different Target Regions by a Common CA3 Input. Cereb Cortex. 2016 Oct;26 (10):4082-100. doi: 10.1093/cercor/bhv211.
Humanes-Valera D, Foffani G, Alonso-Calviño E, Fenández-López E, Aguilar J. (2016). “Dual cortical plasticity after spinal cord injury”. Cerebral Cortex Epub ahead of print. doi:10.1093/cercor/bhw142. IF: 8.66
Alonso-Calviño E, Martínez-Camero I, Fernández-López F, Humanes-Valera D, Foffani G, Aguilar J. (2016), “Increased responses in the somatosensory thalamus immediately after spinal cord injury”. Neurobiology of Disease 87:39-49. doi:10.1016/j.nbd.2015.12.003 IF: 5.078

Cell Physiology Cajal’s Laboratory (IC-CSIC):

Perea G, Gómez R, Mederos S, Covelo A, Ballesteros J, Schlosser L, Hernández-Vivanco A, Martín-Fernández M, Quintana R, Rayan A, Díez A, Fuenzalida M, Agarwal A, Bergles D, Bettler B, Manahan-Vaughan D, Martín ED, Kirchhoff F, Araque A (2016) Activity-dependent switch of GABAergic inhibition into glutamatergic excitation in astrocyte-neuron networks. eLife pii: e20362.
30. Hernandez-Garzón E, Fernandez AM, Perez-Alvarez A, Genis L, Bascuñana P, Fernandez de la Rosa R, Delgado M, Angel Pozo M, Moreno E, McCormick PJ, Santi A, Trueba-Saiz A, Garcia-Caceres C, Tschöp MH, Araque A, Martin ED, Torres Aleman I (2016) The insulin-like growth factor I receptor regulates glucose transport by astrocytes. Glia 64:1964-1971.
Covelo A, Araque A (2015) Lateral regulation of synaptic transmission by astrocytes. Neuroscience Neuroscience. 2016 May 26;323:62-6. doi: 10.1016/j.neuroscience.2015.02.036. Epub 2015 Feb 27.

Other CBBP Publications

DeFelipe J, Rudy B (2017) Neocortical microcircuits. Handbook of Brain Microcircuits. 2nd edition. Gordon Shepherd and Sten Grillner, eds. Oxford University Press, New York. (Submitted).
Atienza, D., Bielza, C., Diaz, J., Larrañaga, P. (2016). “Anomaly detection with a spatio-temporal tracking of the laser spot”. Frontiers in Artificial Intelligence and Applications Series, 284, 137-142, IOS Press.
Diaz, J., Bielza, C., Ocaña, J.L., Larrañaga, P. (2016). “Development of a cyber-physical system based on selective Gaussian naive Bayes model for a self-predict laser surface heat treatment process control”. Machine Leaning for Cyber Physical Systems, 1-8, Springer.
Leguey, I., Bielza, C., Larrañaga, P. (2016). “Tree-structured Bayesian networks for wrapped Cauchy directional distributions”. Lecture Notes in Artificial Intelligence, 9868, 207-216. Springer.
Luengo-Sanchez, S., Bielza, C., Larrañaga, P. (2016). “Hybrid Gaussian and von Mises model-based clustering”. Frontiers in Artificial Intelligence and Applications Series, 285, 855-862. IOS Press.
Ogbechie, A., Díaz-Rozo, A., Larrañaga, P., Bielza, C. (2016). “Dynamic Bayesian network-based anomaly detection for in-process visual inspection of laser surface heat treatment”. Machine Leaning for Cyber Physical Systems, 17-24, Springer.
Córdoba-Sánchez, I., C. Bielza, and P. Larrañaga, “Graphoids and separoids in model theory”, Technical Report TR:UPM-ETSIINF/DIA/2016-1: Universidad Politécnica de Madrid, 2016.

2017 Scientific Publications & Other Publications

2017 CBBP Scientific Publications

Urrecha M, Romero I1, DeFelipe J, Merchán-Pérez A. Influence of cerebral blood vessel movements on the position of perivascular synapses. PLoS One. 2017 Feb 15;12(2):e0172368. doi: 10.1371/journal.pone.0172368.
Pallas-Bazarra N, Kastanauskaite A, Avila J, DeFelipe J, Llorens-Martín M (2017).
GSK-3β Overexpression Alters the Dendritic Spines of Developmentally Generated Granule Neurons in the Mouse Hippocampal Dentate Gyrus. Front Neuroanat. 2017 Mar 10;11:18. doi: 10.3389/fnana.2017.00018. eCollection 2017.
Gonzalez-Riano C, Tapia-González S, García A, Muñoz A, DeFelipe J, Barbas C. (2017) Metabolomics and neuroanatomical evaluation of post-mortem changes in the hippocampus. Brain Struct Funct. 2017 Mar 11. doi: 10.1007/s00429-017-1375-5. [Epub ahead of print]
Anton-Sanchez L, Larrañaga P, Benavides-Piccione R, Fernaud-Espinosa I, DeFelipe J, Bielza C. Three-dimensional spatial modeling of spines along dendritic networks in human cortical pyramidal neurons. PLoS One. 2017 Jun 29;12(6):e0180400. doi: 10.1371/journal.pone.0180400. eCollection 2017.
DeFelipe J (2017). Neuroanatomy and Global Neuroscience. Neuron. 2017 Jul 5;95(1):14-18. doi: 10.1016/j.neuron.2017.05.027.
Santuy A, Rodriguez JR, DeFelipe J, Merchan-Perez A (2017) Volume electron microscopy of the distribution of synapses in the neuropil of the juvenile rat somatosensory cortex. Brain Struct Funct. 2017 Jul 18. doi: 10.1007/s00429-017-1470-7. [Epub ahead of print]
P. Fernandez-Gonzalez, R. Benavides-Picione, C. Bielza, P. Larrañaga, I. Leguey, and J. DeFelipe. “Dendritic-branching angles of pyramidal neurons of the human temporal cortex”, Brain Struct Funct. 2017 May; 222(4):1847-1859. doi: 10.1007/s00429-016-1311-0. Epub 2016 Sep 30
Gómez-Gonzalo M, Martin-Fernandez M, Martínez-Murillo R, Mederos S, Hernández-Vivanco A, Jamison S, Fernandez AP, Serrano J, Calero P, Futch HS, Corpas R, Sanfeliu C, Perea G, Araque A (2016) Neuron-astrocyte signaling is preserved in the ageing brain. Glia, 2017 Apr;65(4):569-580. doi: 10.1002/glia.23112. Epub 2017 Jan 28.
Fernandez, A.M., Hernandez-Garzón, E., Perez-Domper, P., Perez-Alvarez, A., Mederos, S., Matsui, T., Santi, A., Trueba-Saiz, A., García-Guerra, L., Pose-Utrilla, J., Fielitz, J., Olson, E.N., Fernandez de la Rosa, R., Garcia, L.G., Pozo, M.A., Iglesias, T., Araque, A., Soya, H., Perea, G., Martin, E.D., Torres Aleman, I. (2016). Insulin Regulates Astrocytic Glucose Handling Through Cooperation With Insulin-Like Growth Factor I. Diabetes. 2017 Jan;66(1):64-74. doi: 10.2337/db16-0861. Epub 2016 Oct 10.
Cuadrado-Tejedor M, Garcia-Barroso C, Sánchez-Arias JA, Rabal O, Pérez-González M, Mederos S, Ugarte A, Franco R, Segura V, Perea G, Oyarzabal J, Garcia-Osta A. (2016) A First-in-Class Small-Molecule that Acts as a Dual Inhibitor of HDAC and PDE5, and that Rescues Hippocampal Synaptic Impairment in Alzheimer's Disease Mice. Neuropsychopharmacology. 2017 Jan; 42(2):524-539. doi: 10.1038/npp.2016.163. Epub 2016 Aug 23.

2018 Scientific Publications & Other Publications

2018 CBBP Scientific Publications

Luengo-Sanchez, S., I. Fernaud-Espinosa, C. Bielza, R. Benavides-Piccione, P. Larrañaga, and J. DeFelipe, "3D morphology-based clustering and simulation of human pyramidal cell dendritic spines", PLoS Computational Biology, vol. 14, issue 6, e1006221, 2018.
Mihaljevic, B., P. Larrañaga, R. Benavides-Piccione, S..Hill, J. DeFelipe, and C. Bielza, "Towards a supervised classification of neocortical interneuron morphologies", BMC Bioinformatics, vol. 19, issue 1, Article 511, 2018.
Varando, G., R. Benavides-Piccione, A. Muñoz, A. Kastanauskaite, C. Bielza, P. Larrañaga, and J. DeFelipe, "MultiMap: A tool to automatically extract and analyze spatial microscopic data from large stacks of confocal microscopy images", Frontiers in Neuroanatomy, vol. 12. Article 37, 2018.
Leguey I, Benavides-Piccione R, Rojo C, Larrañaga P, Bielza C, DeFelipe J. (2019) Patterns of Dendritic Basal Field Orientation of Pyramidal Neurons in the Rat Somatosensory Cortex. eNeuro. 2019 Jan 17;5(6). pii: ENEURO.0142-18.2018
Aliaga Maraver, J. J., Mata, S., Benavides-Piccione, R., DeFelipe, J., & Pastor, L. (2018). A Method for the Symbolic Representation of Neurons. Frontiers in neuroanatomy, 12, 106. doi:10.3389/fnana.2018.00106
Santuy, A., Turégano-López, M., Rodríguez, J. R., Alonso-Nanclares, L., DeFelipe, J., and Merchán-Pérez, A. (2018). A Quantitative Study on the Distribution of Mitochondria in the Neuropil of the Juvenile Rat Somatosensory Cortex. Cerebral Cortex 28, 3673–3684. doi:10.1093/cercor/bhy159.
Santuy, A., Rodriguez, J. R., DeFelipe, J., and Merchan-Perez, A. (2018). Volume electron microscopy of the distribution of synapses in the neuropil of the juvenile rat somatosensory cortex. Brain Struct Funct 223, 77–90. doi:10.1007/s00429-017-1470-7.
Santuy, A., Rodríguez, J.-R., DeFelipe, J., and Merchán-Pérez, A. (2018). Study of the Size and Shape of Synapses in the Juvenile Rat Somatosensory Cortex with 3D Electron Microscopy. eNeuro 5. doi:10.1523/ENEURO.0377-17.2017.
Rodríguez, J.-R., Turégano-López, M., DeFelipe, J., and Merchán-Pérez, A. (2018). Neuroanatomy from Mesoscopic to Nanoscopic Scales: An Improved Method for the Observation of Semithin Sections by High-Resolution Scanning Electron Microscopy. Front. Neuroanat. 12. doi:10.3389/fnana.2018.00014.
León-Espinosa G, Antón-Fernández A, Tapia-González S, DeFelipe J, Muñoz A (2018) Modifications of the axon initial segment during the hibernation of the Syrian hamster. Brain Struct Funct. 223:4307-4321. doi: 10.1007/s00429-018-1753-7.
León-Espinosa G, Regalado-Reyes M¸ DeFelipe J, and Muñoz A (2018) Changes in neocortical and hippocampal microglial cells during hibernation. Brain Structure and Function 223:1881-1895. doi: 10.1007/s00429-017-1596-7.
Domínguez-Álvaro M; Montero-Crespo M; Blázquez-Llorca L; Insausti R; DeFelipe J; Alonso-Nanclares L (2018) Three-Dimensional Analysis of Synapses in the Transentorhinal Cortex of Alzheimer's disease patients. Acta Neuropathologica Communications 6:20. doi.org/10.1186/s40478-018-0520-6.
Furcila D, DeFelipe J, Alonso-Nanclares L (2018). A study of amyloid-β and phosphotau in plaques and neurons in the hippocampus of Alzheimer’s disease patients. Journal of Alzheimer Disease, 64: 417-435. Doi: 10.3233/JAD-180173
Anton-Sanchez, L., F. Effenberger, C. Bielza, P. Larrañaga, and H. Cuntz, "A regularity index for dendrites -local statistics of a neuron’s input space", PLoS Computational Biology, vol. 14, 11, e1006593, 2018
Benjumeda, M., C. Bielza, and P. Larrañaga, "Learning tractable Bayesian networks in the space of elimination orders", Artificial Intelligence, accepted, 2018
Benjumeda, M., C. Bielza, and P. Larrañaga, "Tractability of most probable explanations in multidimensional Bayesian network classifiers", International Journal of Approximate Reasoning, vol. 93, pp. 74-87, 2018
Leguey, I., P. Larrañaga, C. Bielza, and S. Kato, "A circular-linear dependence measure under Johnson--Wehrly distributions and its application in Bayesian networks.", Information Sciences, accepted, 2019.
Mihaljevic, B., C. Bielza, and P. Larrañaga, "bnclassify: Learning Bayesian Network Classifiers", The R Journal, accepted, 2018.
Sujar, A., Casafranca, J. J., Serrurier, A., & Garcia, M. (2018). Real-time animation of human characters’ anatomy. Computers & Graphics. DOI: 10.1016/j.cag.2018.05.025. 2018
Durkee CA, Covelo A, Lines J, Kofuji P, Aguilar J, Araque A (2019) Gi/o protein-coupled receptors inhibit neurons but activate astrocytes and stimulate gliotransmission. Glia (in press)
García-Cáceres C, Balland E, Prevot V, Luquet S, Woods SC, Koch M, Horvath TL, Yi CX, Chowen JA, Verkhratsky A, Araque A, Bechmann I, Tschöp MH (2019) Role of astrocytes, microglia, and tanycytes in brain control of systemic metabolism. Nature Neuroscience 22:7-14.
Durkee CA, Araque A (2018) Diversity and Specificity of Astrocyte-neuron Communication. Neuroscience. 396: 73-78.
Covelo A, Araque A (2018) Stimulating Astrocytes to Remember. Cell 174: 12-13.
Teravskis PJ, Covelo A, Miller EC, Singh B, Martell-Martínez HA, Benneyworth MA, Gallardo C, Oxnard BR, Araque A, Lee MK, Liao D (2018) A53T Mutant Alpha-Synuclein Induces Tau-Dependent Postsynaptic Impairment Independently of Neurodegenerative Changes. Journal of Neuroscience 38: 9754-9767.
Covelo A, Araque A (2018) Neuronal activity determines distinct gliotransmitter release from a single astrocyte. eLife 7:e32237 DOI: 10.7554/eLife.32237.
Robin LM, Oliveira da Cruz JF, Langlais VC, Martin-Fernandez M, Metna-Laurent M, Busquets-Garcia A, Bellocchio L, Soria-Gomez E, Papouin T, Varilh M, Sherwood MW, Belluomo I, Balcells G, Matias I, Bosier B, Drago F, Van Eeckhaut A, Smolders I, Georges F, Araque A, Panatier A, Oliet SHR, and Marsicano G (2018) Astroglial CB1 receptors determine synaptic D-serine availability to enable recognition memory. Neuron 98:935-944.
Eyal G, Verhoog MB, Testa-Silva G, Deitcher Y, Benavides-Piccione R, DeFelipe J, de Kock CPJ,Mansvelder HD and Segev I (2018) Human Cortical Pyramidal Neurons: From Spines to Spikes via Models. Front. Cell. Neurosci. 12:181. doi: 10.3389/fncel.2018.00181.

2018 Scientific Publications (submitted)

Data Analysis

Atienza, D., C. Bielza, P. Larrañaga, “Efficient anomaly detection in a laser surface heat treatment process by tracking the laser spot”, submitted to IEEE Transactions on Industrial Informatics
Benjumeda, M., S. Luengo, P. Larrañaga, C.Bielza, “Tractable learning of Bayesian networks from partially observed data”, submitted to Pattern Recognition
Benjumeda, M., Y.-L. Tan, K.A. González-Otárula, D. Chandramohan, E.F. Chang, J.A. Hall, C. Bielza, P. Larrañaga, E. Kobayashi, R.C. Knowlton, “Multidimensional Bayesian network classifiers for individualized prediction of temporal lobe epilepsy surgical outcomes”, submitted to Journal of the American Medical Association
Córdoba-Sanchez, I., C. Bielza, P. Larrañaga, “On Gaussian Markov models for conditional independence”, submitted to Journal of Statistical Planning and Inference
Díaz-Rozo, J., C. Bielza, P. Larrañaga, “Gaussian mixture models-based dynamic probabilistic clustering”, submitted to Engineering Applications of Artificial Intelligence
Fernandez-Gonzalez, P., C. Bielza, P. Larrañaga, “Random forests for regression as a weighted sum of k-potential nearest neighbors”, submitted to IEEE Access
Gil-Begue, S., C. Bielza, and P. Larrañaga, "Multi-dimensional Bayesian network classifiers: A survey", submitted to ACM Computing Surveys
Leguey, I., C. Bielza, P. Larrañaga, “Circular Bayesian classifiers using wrapped Cauchy distributions”, submitted to Data & Knowledge Engineering
Puerto-Santana, E., C. Bielza, P. Larrañaga, “Auto regressive asymmetric linear Gaussian hidden Markov models”, submitted to IEEE Pattern Analysis and Machine Intelligence
Varando, G., C. Bielza, P. Larrañaga, E. Riccomagno, “Markov property in generative classifiers, submitted to Journal of Machine Learning Research
Villa, C., C. Bielza, P. Larrañaga, “Feature subset selection over dynamic data: A review”, submitted to ACM Computing Surveys

Visualization (GMRV-URJC & UPM)

Diana Furcila, Marcos García, Cosmin Toader, Juan Morales, Antonio LaTorre, Ángel Rodríguez, Luis Pastor, Javier DeFelipe, Lidia Alonso-Nanclares. InTool Explorer: an interactive exploratory analysis tool for versatile visualizations of neuroscientific data. Frontiers in Neuroanatomy. (Minor changes requested and resubmitted)

Physiology and Functional Modelling

Ortuño T, López-Madrona VJ, Tapia-Gonzalez S, Muñoz A, De Felipe J, Herreras O. Developmental changes in layer-specific field potential sources in the S1HL cortex indicate intracolumnar functional reorganization. Under review (submitted to Q1 journal).

Contact

The Cajal Blue Brain project is managed and developed almost entirely at the Polytechnic University of Madrid (UPM), Campus Montegancedo. Researchers and engineers are located in two locations, the Center for Biomedical Technology (CTB) and the Supercomputing and Visualization Center of Madrid (CeSViMa), CTB being the headquarters project.

If you are interested in the project and / or wish to have more information about it, they can get through social networks, VCard, QR code, or the contact form shown below:

Contact

Cajal Blue Brain Project
Centro de Tecnología Biomédica (CTB)
Campus de Montegancedo, Universidad Politécnica de Madrid
Pozuelo de Alarcon, 28223 Madrid, España

Motivation

Neuronal Forest simulation

The Project (2009-2018)

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BBP Structure

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Neuronize

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Dissemination Material and Press Dossier (2009-2018)

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Publications (2009-2018)

2009 CBBP Scientific Publications

2010 Scientific Publications

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2011 CBBP Scientific Publications

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2012 CBBP Scientific Publications

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2013 CBBP Scientific Publications

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2014 CBBP Scientific Publications

2015 CBBP Scientific Publications

2016 CBBP Scientific Publications

2017 CBBP Scientific Publications

2018 CBBP Scientific Publications

2018 Scientific Publications (submitted)

Data Analysis

Visualization (GMRV-URJC & UPM)

Physiology and Functional Modelling

Contact

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