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74 HBP-funded scientific publications in 2016

Adhikari, P.R., Vavpetič, A., Kralj, J., Lavrač, N., & Hollmen, J. (2016). Explaining Mixture Models through Semantic Pattern Mining and Banded Matrix VisualizationMachine Learning: 1-37.

Cellot, G., Maggi, L., Di Castro, M.A., Catalano, M., Migliore, R., Migliore, M., Scattoni, M.L., Calamadrei, G., & Cherubini, E. (2016). Premature changes in neuronal excitability account for hippocampal network impairment and autistic-like behavior in neonatal BTBR T+tf/J miceScientific Reports: 31696.

Dayan, E., Sella, I., Mukovskiy, A., Douek, Y., Giese, M. A., Malach, R., & Flash, T. (2016). The default mode network differentiates biological from non-biological motionCerebral Cortex, 26(1): 234-245.

Dehghani, N., Peyrache, A., Telenczuk, B., Le Van Quyen, M., Halgren, M., Cash, S., Hatsopoulos, NG., & Destexhe, A. (2016). Dynamic balance of excitation and inhibition in human and monkey neocortexScientific Reports, 6:23176. 

Diamond, A., Nowotny, T., & Schmuker, M. (2016). Comparing neuromorphic solutions in action:implementing a bio-inspired solution to a benchmark classification task on three parallel-computing platformsFrontiers in Neuroscience, 9: 491. 

Diamond, A., Schmuker, M., Berna, A.Z., Trowell, S., & Nowotny, T. (2016). Classifying continuous real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory systemBioinspiration and Biomimetics, 11: 026002.

Dubois, J., Poupon, C., Thirion, B., Simonnet, H., Kulikova, S., Leroy, F., ... & Dehaene-Lambertz, G. (2016). Exploring the early organization and maturation of linguistic pathways in the human infant brainCerebral Cortex26(5): 2283-2298.

Eickhoff, S., Nichols, T.E., Van Horn, J.D., & Turner, A. (2016). Sharing the wealth: Neuroimaging data repositoriesNeuroimage, 124: 1065-1068.

Eising, E., Huisman, S.M., Mahfouz, A., Vijfhuizen, L.S., Anttila, V., Winsvold, B.S., ... & Reinders, M.J. (2016). Gene co-expression analysis identifies brain regions and cell types involved in migraine pathophysiology: a GWAS-based study using the Allen Human Brain AtlasHuman Genetics, 135: 425-439.

Farisco, M., Evers, K., & Salles, A. (2016). Big Science, Brain Simulation, and NeuroethicsAmerican Journal of Bioethics Neuroscience, 7: 28-30. 

Furber, S.B. (2016). Brain-inspired computing. IET Computers and Digital Techniques, doi: 10.1049/iet-cdt.2015.0171. 

Furber, S.B. (2016). Large-scale neuromorphic computing systemsJournal of Neural Engineering, 13: 5. 

Gabitov, E., Manor, D., & Karni, A. (2016). Learning from the other limb's experience: sharing the 'trained' M1 representation of the motor sequence knowledgeJournal of Physiology, 594: 169–188.

Gamberger, D., Zenko, B., Mitelpunkt, A., Schachar, N., & Lavrač, N. (2016). Clusters of male and female Alzheimer's disease patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Brain Informatics, 3: 169-179. 

Gilson, M., Moreno-Bote, R., Ponce-Alvarez, A., Ritter, P., & Deco, G. (2016). Estimation of directed effective connectivity from fMRI functional connectivity hints at asymmetries of cortical connectome. PLoS Computational Biology, 125: e1004762.

Gomes, J.M., Bédard, C., Valtcheva, S., Nelson, M., Khokhlova, V., Pouget, P., Venance, L., Bal, T., & Destexhe, A. (2016). Intracellular impedance measurements reveal non-ohmic properties of the extracellular medium around neuronsBiophysical Journal, 110: 234-246. 

Gonen, T., Soreq, E., Eldar, E., Ben-Simon, E., Raz, G., & Hendler, T. (2016). Human mesostriatal response tracks motivational tendencies under naturalistic goal conflictSocial Cognition and Affective Neuroscience, 110: 961-972. 

Gustus, A., & van der Smagt, P. (2016). Evaluation of joint type modelling in the human handJournal of Biomechanics, doi: 10.1016/j.jbiomech.2016.07.018.

Hänel, C., Weyers, B., Hentschel, B., & Kuhlen, T.W. (2016). Visual quality adjustment for volume rendering in a head-tracked virtual environment. IEEE Transactions on Visualizations and Computer Graphics, 22: 1472-1481. 

Henssen, A., Zilles, K., Palomero-Gallagher, N., Schleicher, A., Mohlberg, H., Gerboga, F., Eickhoff, S.B., Bludau, S., & Amunts, K. (2016). Cytoarchitecture and probability maps of the human medial orbitofrontal cortexCortex, 75: 87-112.

Hesseg, R.M., Gal, C., Karni, A. (2016). Not quite there: skill consolidation in training by doing or observingLearning and Memory, 23: 189-194. 

Hinkel, G., Denninger, O., Krach, S., & Groenda, H. (2016). Experiences with model-driven engineering in neuroroboticsModelling Foundations and Applications, 9764: 217-228.

Jalalvand, E., Robertson, B., Tostivint, H., Wallén, P., & Grillner, S. (2016). The spinal cord has an intrinsic system for the control of pHCurrent Biology, 26: 1346-1351. 

Jalalvand, E., Robertson, B., Wallén, P., & Grillner, S. (2016). Ciliated neurons lining the central canal sense both fluid movement and p through ASIC13Nature Communications, 7: 10002. 

Jonke, Z., Habenschuss, S., & Maass, W. (2016). Solving constraint satisfaction problems with networks of spiking neuronsFrontiers in Neuroscience, 10: 118.

Knight, J.C., Tully, P.J., Kaplan, B.A., Lansner, A., & Furber, S.B. (2016). Large-scale simulations of plastic neural networks on neuromorphic hardwareFrontiers in Neuroanatomy, 10: 37. 

Knoll, A, Röhrbein, F, & Walter, F. (2016). Computation by TimeNeural Processing Letters, 44:103-124.

Krach, S., Hinkel, G., & Denninger, O. (2016). Integration testing of neural robot controllers using formal experiment descriptions based on SCXML. 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems: 3rd Workshop on Engineering Interactive Systems with SCXML.

Lewis, C. M., Bosman, C. A., Womelsdorf, T., & Fries, P. (2016). Stimulus-induced visual cortical networks are recapitulated by spontaneous local and interareal synchronizationProceedings of the National Academy of Sciences, 113(5), E606-E615.

Leguey, I., Bielza, C., Larrañaga, P., Kastanauskaite, A., Rojo, C., Benavides-Piccione, R., & DeFelipe, J. (2016). Dendritic branching angles of pyramidal cells across layers of the juvenile rat somatosensory cortexJournal of Comparative Neurology, 524: 2567-2576 

Lorio, S., Fresard, S., Adaszewski, S., Kherif, F., Chowdhury, R., Frackowiak, R., Ashburner, J., Helms, G., Weiskopf, N., Lutti, A., & Draganski, B. (2016). New tissue priors for improved automated classification of subcortical brain structures on MRINeuroimage, 130: 157-166.

Lorio, S., Kherif, F., Ruef, A., Melie-Garcia, L., Frackowiak, R., Ashburner, J., Helms, G., Lutti, A., & Draganski, B. (2016). Neurobiological origin of spurious brain morphologcal changes: A quantitative MRI study. Human Brain Mapping, 370: 1801-1815. 

Lupascu, C.A., Morabito, A., Merenda, E., Marinelli, S., Marchetti, C., Migliore, R., Cherubini, E., & Migliore, M. (2016). A General Procedure to Study Subcellular Models of Transsynaptic Signaling at Inhibitory SynapsesFrontiers in Neuroinformatics, 10: 23.

Luján, R. (2016). Pre-embedding Methods for the Localization of Receptors and Ion ChannelsReceptor and Ion Channel Detection in the Brain: Methods and Protocols, 191-210.

Luján, R., & Watanabe, M. (2016). Post-embedding Immunohistochemistry in the Localisation of Receptors and Ion ChannelsReceptor and Ion Channel Detection in the Brain: Methods and Protocols, 211-232.

Mahfouz, A., Lelieveldt, B.P., Grefhorst, A., van Weert, L.T., Mol, I.M., Sips, H.C., ... & Meijer, O.C. (2016). Genome-wide coexpression of steroid receptors in the mouse brain: Identifying signaling pathways and functionally coordinated regions. Proceedings of the National Academy of Sceinces of the United States of America, 113: 2738-2743.

Malikovic, A., Amunts, K., Schleicher, A., Mohlberg, H., Kujovic, M., Palomero-Gallagher, N., ... & Zilles, K. (2016). Cytoarchitecture of the human lateral occipital cortex: mapping of two extrastriate areas hOc4la and hOc4lpBrain Structure and Function221(4), 1877-1897.

Mandel, A., Bourguignon, M., Parkkonen, L., & Hari, R. (2016). Sensorimotor activation related to speaker vs. listener role during natural conversationNeuroscience Letters, 614, 99-104.

Mangin, J.F., Lebenberg, J., Lefranc, S., Labra, N., Auzias, G., Labit, ... & Sun, Z.Y. (2016). Spatial normalization of brain mages and beyondMedical Image Analysis, 33: 127-133.

Mensi, S., Hagens, O., Gerstner, W., & Pozzorini, C. (2016). Enhanced sensitivity to rapid input fluctuations by nonlinear thresholf dynamics in neocortical pyramidal neuronsPLoS Computational Biology, 12: e1004761. 

Mensch, A., Varoquaux, G., & Thirion, B. (2016). Compressed online dictionary learning for fast fMRI decomposition. ICML Workshop on Statistics, Machine Learning and Neuroscience.

Osojnik, A., Panov, P., & Džeroski, S. (2016). Comparison of Tree-Based Methods for Multi-target Regression on Data Streams. In International Workshop on New Frontiers in Mining Complex Patterns (17-31). Springer International Publishing.

Panov, P., Soldatova, L. N., & Džeroski, S. (2016). Generic ontology of datatypesInformation Sciences, 329, 900-920.

Papp, E.A., Leergaard, T.B., Csucs, G., & Bjaalie, J.G. (2016). Brain-Wide Mapping of Axonal Connections: Workflow for Automated Detection and Spatial Analysis of Labeling in Microscopic Sections. Frontiers in Neuroinformatics, 10: 11.  

Pavlovic, M., Heinis, T., Tauheed, F., & Karras, P. (2016). TRANSFORMERS: Robust spatial joins on non-uniform data distributions2016 IEEE 32nd International Conference on Data Engineering (ICDE), 673-684.

Pecevski, D., & Maass, W. (2016). Learning probabilistic inference through spike-timing-dependent plasticityeNeuro, 3: 0048-15.2016.

Pfeil, T., Jordan, J., Tetzlaff, T., Grübl, A., Schemmel, J., Diesmann, M., & Meier, K. (2016). Effect of heterogeneity on decorrelation mechanisms in spiking neural networks: a neuromorphic-hardware studyPhysical Review X, 6: 021023.

Porrero, C., Rodriguez-Moreno, J., Quetglas, J.I., Smerdou, C., Furuta, T., & Clascá, F. (2016). A simple and efficient in vivo non-viral RNA transfection method for labelling the whole axonal tree of individual adult long-range projection neuronsFrontiers in Neuroanatomy, 10: 27.

Radke, S., Seidel, E.M., Eickhoff, S.B., Gur, R.C., Schneider, F., Habel, U., & Derntl, B. (2016). When opportunity meets motivation: Neural engagement during social approach is linked to high approach motivationNeuroimage, 127: 267-276. 

Reid, A. T., Bzdok, D., Langner, R., Fox, P. T., Laird, A. R., Amunts, K., ... & Eickhoff, C. R. (2016). Multimodal connectivity mapping of the human left anterior and posterior lateral prefrontal cortexBrain Structure and Function, 221(5), 2589-2605.

Reid, A.T., Lewis, J., Bezgin, G., Khundrakpam, B., Eickhoff, S.B., McIntosh, A.R., Bellec, P., & Evans, A.C. (2016). A cross-modal, cross-species comparison of connectivity measures in the primate brain. Neuroimage, 125: 311-331.

Richter, C., Jentzsch, S., Hostettler, R., Garrido, J.A., Ros, E., Knoll, A.C., Röhrbein, F., van der Smagt, P., & Conradt, J. (2016). Scalability in Neural Control of Musculoskeletal RobotsIEEE Robotics and Automation, doi: 10.1109/MRA.2016.2535081.

Rojo C., Leguey I., Kastanauskaite A., Bielza C., Larrañaga P., DeFelipe J., & Benavides-Piccione R. (2016). Laminar differences in dendritic structure of pyramidal neurons in the juvenile rat somatosensory cortexCerebral Cortex, 26: 2811-2822.

Romani S., Katkov M., & Tsodyks M. (2016). Practice makes perfect in memory recallLearning and Memory, 23: 169-173.

Saygili, G., Staring, M., & Hendriks, E.A. (2016). Confidence Estimation for Medical Image Registration Based On Stereo ConfidencesIEEE Transactions on Medical Imaging, 35(2), 539-549.

Self, M.W., Peters, J.C., Possel, J.K., Reithler, J., Goebel, R., Ris, P., Jeurissen, D., Reddy, L., Claus, S., Baayen, J.C., & Roelfsema, P.R. (2016). The effects of context and attention on spiking activity in human early visual cortexPLoS Biology, 14: e1002420.

Silvestri, L., Costantini, P., Sacconi, L., & Pavone, F.S. (2016). Clearing of fixed tissue: a review from a microscopist's perspectiveJournal of Biomedical Optics, 21: 081205.

Stahl, B.C., Timmermans, J., & Mittelstadt, B.D. (2016). The Ethics of Computing: A Survey of the Computing-Oriented LiteratureACM Computing Surveys, 48: 55.

Tanevski, J., Todorovsk,i L., & Dzerovski, S. (2016). Learning stochastic process-based models of dynamical systems from knowledge and dataBMC Systems Biology, 10: 30.

Teppola, H., Sarkanen, J. R., Jalonen, T. O., & Linne, M. L. (2016). Morphological Differentiation Towards Neuronal Phenotype of SH-SY5Y Neuroblastoma Cells by Estradiol, Retinoic Acid and Cholesterol. Neurochemical Research, 41(4), 731-747.

Toharia, P., Robles, O.D., Fernaud-Espinosa, I., Makarova, J., Galindo, S.E., 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 neuronsFrontiers in Neuroanatomy, 9: 159.

Torre, E., Canova, C., Denker, M., Gerstein, G., Helias, M., & Grün, S. (2016). ASSET: analysis of sequences of synchronous events in massively parallel spike trainsPLoS Computational Biology, 125: e1004939.

van Unen, V., Li, N., Molendijk, I., Temurhan, M., Höllt, T., van der Meulen-de Jong, A.E., ... & Koning, F. (2016). Mass Cytometry of the Human Mucosal Immune System Identifies Tissue- and Disease-Associated Immune SubsetsImmunity, 44: 1227-1239.

Walter, F., Röhrbein, F., & Knoll, A. (2016). Computation by Time. Neural Processing Letters, 44(1): 103-124.

Weiner, K.S., & Zilles, K. (2016). The anatomical and functional specialization of the fusiform gyrusNeuropsychologia, 83: 46-62. 

Yegenoglu, A., Quaglio, P., Torre, E., Grün, S., & Endres, D. (2016). Exploring the usefulness of formal concept analysis for robust detection of spatio-temporal spike patterns in massively parallel spike trains. Graph-Based Representation and Reasoning, 9171: 3-16.

Yetman, M. J., Lillehaug, S., Bjaalie, J. G., Leergaard, T. B., & Jankowsky, J. L. (2016). Transgene expression in the Nop-tTA driver line is not inherently restricted to the entorhinal cortexBrain Structure and Function, 221(4), 2231-2249.

Yousefzadeh, A., Plana, L.A., Temple, S., Serrano-Gotarredona, T., Furber, S.B., & Linares-Barranco, B. (2016). Fast predictive handshaking in synchronous FPGAs for fully asynchronous multisymbol chip links: application to SpiNNaker 2-of-7 linksIEEE Transactions on Circuits and Systems II: Express Briefs, 63: 763-767.

Zehl, L., Jaillet, F., Stoewer, A., Grewe, J., Sobolev, A., Wachtler, T., Brochler, T.G., Riehle, A., Denker, M., & Grün, S. (2016). Handling metadata in a neurophysiology laboratoryFrontiers in Neuroinformatics, 10: 26. 

Zeineh, M.M., Palomero-Gallagher, N., Axer, M., Gräßel, D., Goubran, M., Wree, A., Woods, R., Amunts, K., & Zilles, K. (2016). Direct visualization and mapping of the spatial course of fiber tracts at micsoscopic resolution in the human hippocampusCerebral Cortex, doi: 10.1093/cercor/bhw010.

Zerlaut, Y, Teleńczuk, B, Deleuze, C, Bal, T, Ouanounou, G, & Destexhe, A. (2016). Heterogeneous firing rate response of mouse layer V pyramidal neurons in the fluctation-driven regimeJournal of Physiology, 594: 3791-3808.

Zielasko, D., Horn, S., Freitag, S., Weyers, B., & Kuhlen, T.W. (2016). Evaluation of hands-free HMD-based navigation techniques for immersive data analysis2016 IEEE Symposium on 3D User Interfaces (3DUI), 113-119.

Zielasko, D., Weyers, B., Hentschel, B., & Kuhlen, T.W. (2016). Interactive 3D force-directed edge bundlingEurographics, 35.

Zhou, G., Bourguignon, M., Parkkonen, L., & Hari, R. (2016). Neural signatures of hand kinematics in leaders vs. followers: a dual-MEG study. Neuroimage, 125, 731-738.

Books and book chapters

Farisco, M., & Evers, K. (Eds.). (2016). Neurotechnology and Direct Brain Communication: New insights and responsibilities concerning speechless but communicative subjects. Routledge, New York.

Frégnac, Y., Fournier, J., Gérard-Mercier, F., Monier, C., Pananceau, M., Carelli, P., & Troncoso, X. (2016). The visual brain: computing through multiscale complexityMicro-, Meso- and Macro-Dynamics of the Brain: 43-57.

Harada, H., & Shigemoto, R. (2016). Immunogold protein localization on grid-glued freeze-fracture replicas. In: High Resolution imaging of Proteins and Tissues in Cells: Light and Electron Microscopy Methods and Protocols - Methods in Molecular Biology (Schwartzbach, S.D. & Schikorski, T. (eds)).

Kandel, E.R., Dudai, Y., & Mayford, M.R. (eds). (2016). Learning and Memory: A Cold Spring Harbor Perspectives in Biology Collection. Cold Spring Harbor Press, New York.

Rückert, U. (2016). Brain-Inspired Architectures for Nanoelectronics. In: CHIPS 2020 Vol 2 (Springer; Heidelberg): pp249-274.

Salles, A. Brain-imaging and privacy concerns. (2016). In: Neurotechnology and Direct Brain Communication: New insights and responsibilities concerning speechless but communicative subjects. Routledge, New York, 143-156. 

Zilles, K., Palomero-Gallagher, N, Gräßel, D., Schlömer, P., Cremer, M., Woods, R., Amunts, K., & Axer, M. High-resolution fiber and fiber tract imaging using polarized light microscopy in the human, monkey, rat, and mouse brain. In: Axons and Brain Architecture (Rockland, K. ed). Elsevier, London.

Other relevant publications

Aicardi C, Del Savio L, Dove ES, Lucivero F, Tempini N, Prainsack P. (2016). Emerging ethical issues regarding digital health data. On the World Medical Association Draft Declaration on Ethical Considerations Regarding Health Databases and BiobanksCroatian Medical Journal, 57: 207-213.

Allegra Mascaro, A.L., Sacconi, L., Silvestri, L., Knott, G., & Pavone, F.S. (2016). Multi-modal optical imaging of the cerebellum in animalsCerebellum, 15: 18-20.

Bludau, S., Bzdok, D., Gruber, O., Kohn, N., Riedl, V., Sorg, C., Palomero-Gallagher, N., Hoffstaedter, F., Amunts, K., & Eickhoff, S.B. (2016). Medial Prefrontal Aberrations in Major Depressive Disorder Revealed by Cytoarchitectonically Informed Voxel-Based MorphometryAmerican Journal of Psychiatry, 173: 291-298.

Calì, C., Baghabra, J., Boges, D.J., Holst, G.R., Kreshuk, A., Hamprecht, F.A., Srinivasan, M., Lahväslaiho, H., & Magistretti, P.J. (2016). Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissuesJournal of Comparative Neurology, 524: 23-38.

Christen, M., Biller-Andorno, N., Bringedal, B., Grimes, K., Savulescu, J., &  Walter, H. (2016). Ethical Challenges of Simulation-Driven Big NeuroscienceAmerican Journal of Bioethics Neuroscience, 7: 5-17.

Evers, K. (2016). Neurotechnological assessment of consciousness disorders: five ethical imperativesDialogues in Clinical Neuroscience, 18: 155-162.

Farisco, M., Evers, K., & Salles, A. (2016). Big Science, Brain Simulation, and NeuroethicsAmerican Journal of Bioethics Neuroscience, 7: 28-30.

Gerard-Mercier, F., Carelli, P.V., Pananceau, M., Troconso, X.G., & Frégnac, Y. (2016). Synaptic correlates of low-level perception in V1Journal of Neuroscience, 36: 3925-3942.

Harada, H., & Shigemoto, R. (2016). High-resolution localization of membrane proteins by SDS-digested freeze-fracture replica labeling (SDS-FRL)Receptor and Ion Channel Detection in the Brain, 110: 233-245.

Løkkegaard, A., Herz, D.M., Haagensen, B.M., Lorentzen, A.K., Eickhoff, S.B., & Siebner, H.R. (2016). Altered sensorimotor activation patterns in idiopathic dystonia-an activation likelihood estimation meta-analysis of functional brain imaging studiesHuman Brain Mapping, 37: 547-557.

Mathys, C., Caspers, J., Langner, R., Südmeyer, M., Grefkes, C., Reetz, K., Moldovan, A.S., Michely, J., Heller, J., Eickhoff, C.R., Turowski, B., Schnitzler, A., Hoffstaedter, F., & Eickhoff, S.B. (2016). Functional Connectivity Differences of the Subthalamic Nucleus Related to Parkinson's DiseaseHuman Brain Mapping, 37: 1235-1253.

Muhle-Karbe, P.S., Derrfuss, J., Lynn, M.T., Neubert, F.X., Fox, P.T., Brass, M., & Eickhoff, S.B. (2016). Co-Activation-Based Parcellation of the Lateral Prefrontal Cortex Delineates the Inferior Frontal Junction AreaCerebral Cortex, 26: 2225-2241.

Sallin, K., Lagercrantz, H., Evers, K., Engström, I., & Petrovic P. (2016). Resignation syndrome: catatonia? Culture-bound? Frontiers in Behavioral Neuroscience, 10: 7.

Salomon, R., Galli, G., Lukowska, M., Faivre, N., Ruiz, J.B., & Blanke, O. (2016). An invisible touch: body-related multisensory conflicts modulate visual consciousnessNeuropsychologia, 88: 131-139.

Schellenberger Costa, M., Born, J., Claussen, J.C., & Martinetz, T. (2016). Modeling the effect of sleep regulation on a neural mass modelJournal of Computational Neuroscience, 41: 15-28.


159 HBP-funded scientific publications in 2015

Allegra Mascaro, A.L., Costantini, I., Margoni, E., Iannello, G., Bria, A., Sacconi, L., & Pavone, F.S. (2015). Label-free near-infrared reflectance microscopy as a complimentary tool for two-photon fluorescence brain imagingBiomedical Optics Express, 6: 4483-4492.

Alvanaki, F., Goncalves, R., Ivanova, M., Kersten, M., & Kyzirakos, K. (2015). GIS navigation boosted by column storesProceedings of the VLDB Endowment - Proceedings of the 41st International Conference on Very Large Data Bases, 8: 1956-1959.

Amunts, K., & Zilles, K. (2015). Architectonic mapping of the human brain beyond BrodmannNeuron, 88: 1086-1107.

Aransay, A., Rodriguez-López, C., García-Amado, M., Clascá, F., & Prensa, L. (2015). Long-range projection neurons of the mouse ventral tegmental area: a single-cell axon tracing analysisFrontiers in Neuroanatomy, 9: 59.

Arnulfo, G., Hirvonen, J., Nobili, L., Palva, S., & Palva, J. M. (2015). Phase and amplitude correlations in resting-state activity in human stereotactical EEG recordingsNeuroImage112: 114-127.

Arnulfo, G., Narizzano, M., Cardinale, F., Fato, M. M., & Palva, J. M. (2015). Automatic segmentation of deep intracerebral electrodes in computed tomography scansBMC Bioinformatics16(1): 99.

Babaei, S., Mahfouz, A., Hulsman, M., Lelieveldt, B. P., de Ridder, J., & Reinders, M. (2015). Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse CortexPLoS Computational Biology11(5): e1004221.

Bakker, R., Tiesinga, P., & Kötter, R. (2015). The scalable brain atlas: Instant web-based access to public brain atlases and related contentNeuroinformatics, 13: 353-366.

Bavandpour, M., Soleimani, H., Linares-Barranco, B., Abbott, D., & Chua, L.O. (2015). Generalized reconfigurable memristive dynamical systems (MDS) for neuromorphic applicationsFrontiers in Neuroscience, 9: 409.

Bergström, F., & Eriksson, J. (2015). The conjunction of non-consciously perceived object identity and spatial position can be retained during a visual short-term memory taskFrontiers in Psychology6: 1470.

Besold, T. R., & Kühnberger, K. U. (2015). Proceedings of the workshop on "Neural-Cognitive Integration"PICS Publications of the Institute of Cognitive Science, 3.

Blanke, O., Slater, M., Serino, A. (2015). Behavioral, neural, and computational principles of bodily self-consciousnessNeuron, 88: 145-166. 

Bosch, C., Martinez, A., Masachs, N., Teixeira, C.M., Fernaud, I., Ulloa, F., Pérez-Martínez, E., Lois, C., Comella, J.X., 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 neuronsFrontiers in Neuroanatomy, 9: 60.

Brigham, M., & Destexhe, A. (2015). Nonstationary filtered shot-noise processes and applications to neuronal membranesPhysical Review E, Statistical, Nonlinear, and Soft Matter Physics, 91: 062102.

Brosch, T., Neumann, H., & Roelfsema, P. R. (2015). Reinforcement Learning of Linking and Tracing Contours in Recurrent Neural NetworksPLoS Computational Biology11(10): e1004489.

Bruce, N.J., Kokh, D.B., Ozboyaci, M., & Wade, R.C. (2015). Modelling of Solvation Effects for Brownian Dynamics Simulation of Biomolecular RecognitionComputational Trends in Solvation and Transport in Liquids, 28: 259-280.

Burms, J., Caluwaerts, K., & Dambre, J. (2015). Online unsupervised terrain classification for a compliant tensegrity robot using a mixture of echo state networksInternational Conference on Robotics and Automation (ICRA): 4252-4257.

Burms, J., Caluwaerts, K., & Dambre, J. (2015). Reward-modulated Hebbian plasticity as leverage for partially embodied control in compliant robotics. Frontiers in Neurorobotics9: 9.

Bzdok, D., Eickenberg, M., Grisel, O., Thirion, B., & Varoquaux, G. (2015). Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging DataNeural Information Processing Systems 28.

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Ray, K.L., Zald, D.H., Bludau, S., Riedel, M.C., Bzdok, D., Yanes, J., Falcone, K.E., Amunts, K., Fox, P.T., Eickhoff, S.B., & Laird, A.R. (2015). Co-activation based parcellation of the human frontal poleNeuroimage, 123: 200-211.

Reckfort, J., Wiese, H., Pietrzyk, U., Zilles, K., Amunts, K., & Axer, M. (2015). A multiscale approach for the reconstruction of the fiber architecture of the human brain based on 3D-PLIFrontiers in Neuroanatomy9: 118.

Reid, A. T., Bzdok, D., Langner, R., Fox, P. T., Laird, A. R., Amunts, K., Eickhoff, S. B. & Eickhoff, C. R. (2015). Multimodal connectivity mapping of the human left anterior and posterior lateral prefrontal cortex. Brain Structure and Function, 221(5): 1-17.

Reimann, M.W., King, J.G., Muller, E.B., Ramaswamy, S., & Markram, H. (2015). An algorithm to predict the connectome of neural microcircuitsFrontiers in Computational Neuroscience, 9: 120.

Richardet, R., Chappelier, J. C., Telefont, M., & Hill, S. (2015). Large-scale extraction of brain connectivity from the neuroscientific literatureBioinformatics, 31(10), 1640-1647.

Saïghi, S., Mayr, C. G., Serrano-Gotarredona, T., Schmidt, H., Lecerf, G., Tomas, J., Grollier, J., Boyn, S., Vincent, A. F., Querlioz, D., La Barbera, S., Alibart, F., Vuillaume, D., Bichler, O., Gamrat, C. & Linares-Barranco, B. (2015). Plasticity in memristive devices for spiking neural networksFrontiers in Neuroscience9: 51.

Saygili, G., Staring, M., & Hendriks, E. (2015). Confidence Estimation for Medical Image Registration Based On Stereo ConfidencesIEEE Transactions on Medical Imaging, 35(2) : 539-549.

Schneider, M., Hirsch, S., Weber, B., Székely, G., & Menze, B. H. (2015). Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filtersMedical Image Analysis19(1): 220-249.

Self, M. W., Mookhoek, A., Tjalma, N., & Roelfsema, P. R. (2015). Contextual effects on perceived contrast: Figure–ground assignment and orientation contrastJournal of Vision15(2): 2.

Serrano-Gotarredona, T., & Linares-Barranco, B. (2015). Poker-DVS, MNIST-DVS. Their history, how they were made, and other detailsFrontiers in Neuroscience, 9: 481.

Serrano-Gotarredona, T., Linares-Barranco, B., Galluppi, F., Plana, L., & Furber, S. (2015, May). ConvNets experiments on SpiNNaker2015 IEEE International Symposium on Circuits and Systems (ISCAS): 2405-2408.

Silvestri, L., Paciscopi, M., Soda, P., Biamonte, F., Iannello, G., Frasconi, P., & Pavone, F. S. (2015). Quantitative neuroanatomy of all Purkinje cells with light sheet microscopy and high-throughput image analysis. Frontiers in Neuroanatomy9: 68.

Stromatias, E., Neil, D., Galluppi, F., Pfeiffer, M., Liu, S.-C., & Furber, S. (2015). Scalable Energy-Efficient, Low-Latency Implementations of Spiking Deep Belief Networks on SpiNNaker2015 International Joint Conference on Neural Networks: 1-8.

Stromatias, E., Neil, D., Pfeiffer, M., Galluppi, F., Furber, S.B., & Liu, S.-C. (2015). Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platformsFrontiers in Neuroscience, 9: 222.

Sutmann, G., Marx, D., Grotendorst, J., & Gompper, G. (2015). Computational Trends in Solvation and Transport in Liquids-Lecture Notes (No. FZJ-2015-02178). Schriften des Forschungszentrums Jülich, IAS Series, 28: 1-631.

Tauheed, F., Heinis, T., & Ailamaki, A. (2015, May). THERMAL-JOIN: A Scalable Spatial Join for Dynamic WorkloadsProceedings of the 2015 ACM SIGMOD International Conference on Management of Data: 939-950.

Tellmann, S., Bludau, S., Eickhoff, S. B., Mohlberg, H., Minnerop, M., & Amunts, K. (2015). Cytoarchitectonic mapping of the human brain cerebellar nuclei in stereotaxic spaceFrontiers in Neuroanatomy9, 54.

Teppola, H., Sarkanen, J. R., Jalonen, T. O., & Linne, M. L. (2015). Morphological Differentiation Towards Neuronal Phenotype of SH-SY5Y Neuroblastoma Cells by Estradiol, Retinoic Acid and Cholesterol. Neurochemical Research, 41(4): 1-17.

Tsetsos, K., Pfeffer, T., Jentgens, P., & Donner, T.H. (2015). Action planning and the timescale of evidence accumulationPLoS One, 10: e0129473.

Thirion, B., Pedregosa, F., Eickenberg, M., & Varoquaux, G. (2015). Correlations of correlations are not reliable statistics: implications for multivariate pattern analysis2015 ICML Workshop on Statistics, Machine Learning and Neuroscience (Stamlins).

Tiesinga, P., Bakker, R., Hill, S., & Bjaalie, J. G. (2015). Feeding the human brain modelCurrent Opinion in Neurobiology32: 107-114.

van Albada, S. J., Helias, M., & Diesmann, M. (2015). Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlationsPLoS Comput Biol 11(9): e1004490.

Vannucci, L., Ambrosano, A., Cauli, N., Albanese, U., Falotico, E., Ulbrich, S., Pfotzer, L., Hinkel, G., Denninger, O., Peppicelli, D., Guyot, L., Von Arnim, A., Deser, S., Maier, P., Dillman, R., Klinker, G., Levi, P., Knoll, A., Gewaltig, M.-O., & Laschi, C. (2015). A visual tracking model implemented on the iCub robot as a use case for a novel neurorobotic toolkit integrating brain and physics simulationIEEE-RAS 15th International Conference on Humanoid Robots, 2015: 1179-1184.

Venetis, T., & Vassalos, V. (2015). Data Integration in the Human Brain ProjectData Integration in the Life Sciences9162: 28-36.

Venetis, T., Ailamaki, A., Heinis, T., Karpathiotakis, M., Kherif, F., Mitelpunkt, A., & Vassalos, V. (2015). Towards the Identification of Disease SignaturesBrain Informatics and Health, 9250: 145-155.

Vogginger, B., Schüffny, R., Lansner, A., Cederström, L., Partzsch, J., & Höppner, S. (2015). Reducing the computational footprint for real-time BCPNN learningFrontiers in Neuroscience9: 2.

Walter, F., Röhrbein, F., & Knoll, A. (2015). Neuromorphic implementations of neurobiological learning algorithms for spiking neural networksNeural Networks 72: 152-167.

Wang, L., Uhrig, L., Jarraya, B., & Dehaene, S. (2015). Representation of numerical and sequential patterns in macaque and human brainsCurrent Biology25(15): 1966-1974.

Wybo, W.A., Boccalini, D., Torben-Nielsen, B., & Gewaltig, M.-O. (2015).  A sparse reformulation of the Green's function formalism allows efficient simulations of morphological neuron modelsNeural Computation, 27: 2587-2622.

Yetman, M. J., Lillehaug, S., Bjaalie, J. G., Leergaard, T. B., & Jankowsky, J. L. (2015). Transgene expression in the Nop-tTA driver line is not inherently restricted to the entorhinal cortexBrain Structure and Function, 221(4):1-19.

Yousefzadeh, A., Serrano-Gotarredona, T., & Linares-Barranco, B. (2015, June). Fast Pipeline 128× 128 pixel spiking convolution core for event-driven vision processing in FPGAs2015 International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP): 1-8.

Yu, X., Martinez, M., Gable, A. L., Fuller, J. C., Bruce, N. J., Richter, S., & Wade, R. C. (2015). webSDA: a web server to simulate macromolecular diffusional associationNucleic Acids Research43(W1): W220-W224.

Zacharatou, E. T., Tauheedz, F., Heinis, T., & Ailamaki, A. (2015, June). RUBIK: efficient threshold queries on massive time seriesProceedings of the 27th International Conference on Scientific and Statistical Database Management:18.

Zachariou, M., Roberts, M., Lowet, E., de Weerd, P., Hadjipapas, A. (2015). Contrast-dependent modulation of gamma rhythm in v1: a network modelBMC Neuroscience, 16(Suppl 1): O10.

Zakiewicz, I. M., Majka, P., Wójcik, D. K., Bjaalie, J. G., & Leergaard, T. B. (2015). Three-Dimensional Histology Volume Reconstruction of Axonal Tract Tracing Data: Exploring Topographical Organization in Subcortical Projections from Rat Barrel CortexPloS one10(9): e0137571.

Zenke, F., Agnes, E.J., & Gerstner, W. (2015). Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networksNature Communications, 6: 6922.

Zhang, Y. F., Liu, L. X., Cao, H. T., Ou, L., Qu, J., Wang, Y., & Chen, J. G. (2015). Otx1 promotes basal dendritic growth and regulates intrinsic electrophysiological and synaptic properties of layer V pyramidal neurons in mouse motor cortexNeuroscience285: 139-154.

Zhdanov, A., Nurminen, J., Baess, P., Hirvenkari, L., Jousmäki, V., Mäkelä, J.P., Mandel, A., Meronen, L., Hari, R., & Parkkonen, L. (2015). An internet-based real-time audiovisual link or dual MEG recordingsPLoS One, 10: e0128485.

Books and book chapters

Kralj, J., Valmarska, A., Grčar, M., Robnik-Šikonja, M., & Lavrač, N. (2015). Analysis of Text-Enriched Heterogeneous Information NetworksBig Data Analysis: New Algorithms for a New Society, 16: 115-139.


Kralj, J., Valmarska, A., Robnik-Šikonja, M., & Lavrač, N. (2015). Mining Text Enriched Heterogeneous Citation NetworksAdvances in Knowledge Discovery and Data Mining, 9077: 672-683.


Levatić, J., Ceci, M., Kocev, D., & Dzeroski, S. (2015). Semi-supervised Learning for Multi-target RegressionNew Frontiers in Mining Complex Patterns, 8983: 3-18.

Mangin, J.-F., Auzias, G., Coulon, O., Sun, Z.Y., Rivière, D., & Régis, J. (2015). Sulci as landmarks. In: Brain Mapping: An Encyclopedic Reference (Toga, A.W,. ed), Academic Press: Elsevier: pp45-52.

Mangin, J.-F., Perrot, M., Operto, G., Cachia, A., Fischer, C., lLfèvre, J., & Rivière, D. (2015). Sulcus identification and labellingIn: Brain Mapping: An Encyclopedic Reference (Toga, A.W., ed), Academic Press: Elsevier: pp365-371.

Osojnik, A., Panov, P., & Dzeroski, S. (2015). Multi-label Classification via Multi-target Regression on Data StreamsDiscovery Science, 9356: 170-185.


Rast, A.D., Stokes, A.B., Davies, S., Adams, S.V., Akolkar, H., Lester, D.R., Bartolozzi, C., Cangelosi, A., & Furber, S. (2015). Transport-independent protocols for universal AER communicationsLecture Notes in Computer Science, 9492: 675-684.


Rodriguez-Lujan, L., Bielza, C., & Larrañaga, P. (2015). Regularized multivariate von Mises distributionAdvances in Artificial Intelligence, 9422: 25-35.


Schneider, M., Hirsch, S., Weber, B., Székeley, G., & Menze, B.H. (2015). TGIF: Topological gap in-fill for vascular networksMedical Image Computing and Computer-Assisted Intervention - MICCAI 2014, 8674: 89-96.


Sorokin, A., Sorokina, O., & Armstrong, J.D. (2015). Rkappa: statistical sampling suite for Kappa modelsHybrid Systems Biology, 7699: 128-142.


Tauheed, F., Heinis, T., & Ailamaki, A. (2015). Configuring spatial grids for efficient main memory joinsData Science, 9147: 199-205.


Vannucci, L., Falotico, E., Di Lecce, N., Dario, P., & Laschi, C. (2015). Integrating feedback and predictive control in a bio-inspired model of visual pursuit implemented on a humanoid robotBiomimetic and Biohybrid Systems, 9222: 256-267.

Other relevant publications

Abdellah, M., Bilgili, A., Eilemann, S., Markram, H., & Schürmann, F. (2015). Physically-based in silico light sheet microscopy for visualizing fluorescent brain models5th Symposium on Biological Data Visualization (BioVis) 2015

Abdellah, M., Bilgili, A., Eilemann, S., Markram, H., & Schürmann, F. (2015). Physically-based in silico light sheet microscopy for visualizing fluorescent brain modelsBMC Bioinformatics, 16 Suppl 11: S8.

Akolkar, H., Meyer, C., Clady, X., Marre, O., Bartolozzi, C., Panzeri, S., & Benosman, R. (2015). What Can Neuromorphic Event-Driven Precise Timing Add to Spike-Based Pattern Recognition? Neural Computation, 27: 561-593.

Armendariz, M., Erb, J., De Martino, F., Formisano, E., & Vanduffel, W. (2015). Encoding of natural sounds in human and monkey auditory cortexFrontiers in Neuroinformatics: Second Belgian Neuroinformatics Congress.

Ben-Yakov, A., Dudai, Y., & Mayford, M.R. (2015). Memory retrieval in mice and menCold Spring Harbor Perspectives in Biology, 7: a021790.

Bettinardi, R. G., Tort-Colet, N., Ruiz-Mejias, M., Sanchez-Vives, M. V., & Deco, G. (2015). Gradual emergence of spontaneous correlated brain activity during fading of general anesthesia in rats: Evidences from fMRI and local field potentialsNeuroImage114: 185-198.

Bill, J., Buesing, L., Habenschuss, S., Nessler, B., Maass, W., & Legenstein, R. (2015). Distributed Bayesian computation and self-organized learning in sheets of spiking neurons with local lateral inhibitionPLoS One, 10: e0134356.

Boccara, C. N., Kjonigsen, L. J., Hammer, I. M., Bjaalie, J. G., Leergaard, T. B., & Witter, M. P. (2015). A three‐plane architectonic atlas of the rat hippocampal regionHippocampus, 25: 838-857.

Caspers, J., Palomero-Gallagher, N., Caspers, S., Schleicher, A., Amunts, K., & Zilles, K. (2015). Receptor architecture of visual areas in the face and word-form recognition region of the posterior fusiform gyrusBrain Structure and Function, 220: 205-219.

Cieslik, E.C., Mueller, V.I., Eickhoff, C.R., Langner, R., & Eickhoff, S.B. (2015). Three key regions for supervisory attentional control: Evidence form neuroimaging meta-analysesNeuroscience and Biobehavioral Reviews, 4: 22-34.

Cijvat, R., Manegold, S., Kersten, M., Klau, G.W., Schönhuth, A., Marschall, T., & Zhang, Y. (2015). Genome sequence analysis with MonetDB: A case study on Ebola virus diversityDatenbank Spektrum, 15: 185-191.

Cohen, N., Pell, L., Edelson, M.G., Ben-Yakov, A., Pine, A., & Dudai, Y. (2015). Peri-encoding predictors of memory encoding and consolidationNeuroscience and Biobehavioral Reviews, 50: 128-142.

Cremer, J.N., Amunts, K., Schleicher, A., Palomero-Gallagher, N., Piel, M., Rösch, F., & Zilles, K. (2015). Changes in the expression of neurotransmitter receptors in Parkin and DJ-1 knockout mice--A quantitative multireceptor studyNeuroscience, 311: 539-551.

Cremer, J.N., Amunts, K., Graw, J., Piel, M., Rösch, F., & Zilles, K. (2015). Neurotransmitter receptor density changes in Pitx3ak mice--a model relevant to Parkinson's diseaseNeuroscience, 285: 11-23.

Dehaene, S., Dudai, Y., & Konen, C. (2015). Cognitive architecturesNeuron, 88: 1.

Dudai, Y., Karni, A., & Born, J. (2015). The consolidation and transformation of memoryNeuron, 88: 20-32.

Edelson, M.G., Shemesh, M., Weizman, A., Yariv, S., Sharot, T., & Dudai, Y. (2015). Opposing effects of oxytocin on overt compliance and lasting changes to memoryNeuropsychopharmacology, 40: 966-973.

Eickhoff, S.B., Thirion, B., Varoquaux, G., & Bzdok, D. (2015). Connectivity-based parcellation: critique and implicationsHuman Brain Mapping, 36: 4771-4792.

Faivre, N., Salomon, R., & Blanke, O. (2015). Visual consciousness and bodily self-consciousnessCurrent Opinion in Neurology, 28: 23-28.

Farisco, M., Laureys, S., & Evers, K. (2015). Externalization of Consciousness. Scientific Possibilities and Clinical ImplicationsCurrent Topics in Behavioural Neurosciences, 19: 205-222.

Fernandez-Gonzalez, P., Bielza, C., & Larranaga, P. (2015). Multidimensional classifiers for neuroanatomical dataICML Workshop on Statistics, Machine Learning and Neuroscience.

Galluppi, F., Lagorce, X., Stromatias, E., Pfeiffer, M., Plana, L.A., Furber, S.B., & Benosman, R.B. (2015). A framework for plasticity implementation on the SpiNNaker neural architectureFrontiers in Neuroscience, 8: 429.

Gardner, B., Sporea, I., & Grüning, A. (2015). Encoding Spike Patterns in Multilayer Spiking Neural NetworksarXiv

Himberg, T., Hirvenkari, L., Mandel, A., & Hari, R. (2015). Word-by-word entrainment of speech rhythm during joint story buildingFrontiers in Psychology, 6: 797.

Javdani, F., Holló, K., Hegedüs, K., Kis, G., Hegyi, Z., Dócs, K., Kasugai, Y., Fukazawa, Y., Shigemoto, R., & Antal, M. (2015). Differential expression patterns of K(+) /Cl(-) cotransporter 2 in neurons within the superficial spinal dorsal horn of ratsJournal of Comparative Neurology, 523: 1967-1983.

Jolivet, R., Coggan, J.S., Allaman, I., & Magistretti, P.J. (2015). Multi-timescale modeling of activity-dependent metabolic coupling in the neuron-glia-vasculature ensemblePLoS Computational Biology, 11: e1004036.

Kargin, Y., Kersten, M., Manegold, S., & Pirk, H. (2015). The DBMS - your big data sommelier2015 IEEE 31st International Conference on Data Engineering (ICDE): 1119-1130.

Kersten, M. (2015). Big data space fungusConference on Innovative data Systems Research (CIDR).

Kogler, L., Müller, V.I., Chang, A., Eickhoff, S.B., Fox, P.T., Gur, R.C., & Derntl, B. (2015). Psychosocial versus physiological stress - Meta-analyses on deactivations and activations of the neural correlates of stress reactionsNeuroImage, 119: 235-251.

Lorteije, J.A.M., Zylberberg, A., Ouelette, B.G., De Zeeuw, C.I., Sigman, M., & Roelfsema, P.R. (2015). The formation of hierarchical decisions in the visual cortexNeuron, 87: 1344-1356.

Ludmer, R., Edelson, M.G., & Dudai, Y. (2015). The Naïve and the Distrustful: state dependency of hippocampal computations in manipulative memory distortionHippocampus, 25: 240-252.

Luján, R., & Aguado, C. (2015). Localization and targeting of GIRK channels in mammalian central neuronsInternational Review of Neurobiology, 123: 161-200.

Maass, W. (2015). To spike or not to spike: that is the questionProceedings of the IEEE, 103: 2219-2224.

Magistretti, P.J., & Allaman, I. (2015). A cellular perspective on brain energy metabolism and functional imagingNeuron, 86: 883-901.

Mansouri, M., Kasugai, Y., Fukazawa, Y., Bertaso, F., Raynaud, F., Perroy, J., Fagni, L., Kaufmann, W.A., Watanabe, M., Shigemoto, R., & Ferraguti, F. (2015). Distinct subtypic localization of type 1 metabotropic glutamate receptors at glutamatergic and GABAergic synapses in the rodent cerebellar cortex. European Journal of Neuroscience, 41: 157-167.

Marre, O., Botella-Soler, V., Simmons, K.D., Mora, T., Tkačik, G., & Berry, M.J. 2nd. (2015). High accuracy decoding of dynamical motion from a large retinal populationPLoS Computational Biology, 11: e1004304.

Menzel, M., Dohmen, M., De Raedt, H., Michielsen, K., Amunts, K., & Axer, M. (2015). Simulation-based validation of the physical model in 3D polarized light imagingOptics in the Life Sciences: JT3A.33.

Menzel, M., Michielsen, K., De Raedt, H., Reckfort, J., Amunts, K., & Axer M. (2015). A Jones matrix formalism for simulating three-dimensional polarized light imaging of brain tissueJournal of the Royal Society Interface, 12: 20150734.

Mühleisen, H., Kersten, M., & Manegold, S. (2015). Capturing the laws of (data) nature7th Biennial Conference on Innovative Data Systems Research (CIDSR).

Navaridas, J., Luján, M., Plana, L.A., Temple, S., & Furber, S.B. (2015). SpiNNaker: enhanced multicast routingParallel Computing, 45: 49-66.

Nettekoven, C., Volz, L. J., Leimbach, M., Pool, E. M., Rehme, A. K., Eickhoff, S. B., Fink, G. R. & Grefkes, C. (2015). Inter-individual variability in cortical excitability and motor network connectivity following multiple blocks of rTMSNeuroImage,118: 209-218.

New, A. B., Robin, D. A., Parkinson, A. L., Eickhoff, C. R., Reetz, K., Hoffstaedter, F., Mathys, C., Sudmeyer, M., Michely, J., Caspers, J., Grefkes, C., Larson, C. R., Ramig, L. O., Fox, P. T. & Eickhoff, S. B. (2015). The intrinsic resting state voice network in Parkinson's diseaseHuman Brain Mapping36: 1951-1962.

Noy, N., Bickel, S., Zion-Golumbic, E., Harel, M., Golan, T., Davidesco, I., Schevon, C.A., McKhann, G.M., Goodman, R.R.,  Schroeder, C.E., Mehta, A.D., & Malach, R. (2015). Ignition's glow: ultra-fast spread of global cortical activity accompanying local "ignitions" in visual cortex during conscious visual perceptionConsciousness and Cognition, 35: 206-224.

Orchard, G., Lagorce, X., Posch, C., Furber, S. B., Benosman, R., & Galluppi, F. (2015, May). Real-time event-driven spiking neural network object recognition on the SpiNNaker platform2015 IEEE International Symposium on Circuits and Systems (ISCAS): 2413-2416.

Patera, A., Astolfo, A., Mader, K.S., Schneider, M., Weber, B., & Stampanoni, M. (2015). Towards the reconstruction of the mouse brain vascular networks with high-resolution synchtrotron radiation X-ray tomographic microscopyMedical Applications of Synchrotron Radiation (MASR2015), poster.

Patera, A., Astolfo, A., Mader, K.S., Schneider, M., Weber, B., & Stampanoni, M. (2015). Towards the reconstruction of the mouse brain vascular networks with high-resolution synchtrotron radiation X-ray tomographic microscopy2nd International Conference on Tomography of Materials and Structures (ICTMS) 2015, poster. 

Pessiglione, M., & Delgado, M.R. (2015). The good, the bad and the brain: neural correlates of appetitive and aversive values underlying decision makingCurrent Opinion in Behavioral Sciences, 5: 78-84.

Poeppl, T. B., Eickhoff, S. B., Fox, P. T., Laird, A. R., Rupprecht, R., Langguth, B., & Bzdok, D. (2015). Connectivity and functional profiling of abnormal brain structures in pedophiliaHuman Brain Mapping36: 2374-2386.

Ponce-Alvarez, A., Deco, G., Hagmann, P., Romani, G. L., Mantini, D., & Corbetta, M. (2015). Resting-State Temporal Synchronization Networks Emerge from Connectivity Topology and HeterogeneityPLoS Computational Biology11: e1004100.

Pool, E. M., Rehme, A. K., Eickhoff, S. B., Fink, G. R., & Grefkes, C. (2015). Functional resting-state connectivity of the human motor network: Differences between right-and left-handersNeuroImage109: 298-306.

Rehme, A.K., Volz, L.J., Feis, D.L., Eickhoff, S.B., Fink, G.R., & Grefkes, C. (2015). Individual prediction of chronic motor outcome in the acute post-stroke stage: Behavioral parameters versus functional mappingHuman Brain Mapping, 36: 4553-4565.

Reig, R., Zerlaut, Y., Vergara, R., Destexhe, A., & Sanchez-Vives, M.V. (2015). Gain modulation of synaptic inputs by network state in auditory cortex in vivoJournal of Neuroscience, 35: 2689-2702.

Rempfler, M., Schneider, M., Ielacqua, G.D., Xiao, X., Stock, S.R., Klohs, J., Székeley, G., Andres, B., & Menze, B.H. (2015).  Reconstructing cerebrovascular networks under local physiological constraints by integer programmingMedical Image Analysis, 25: 86-94.

Schroeter, M.L., Bzdok, D., Eickhoff, S.B., & Neumann, J. (2015). Frontomedian cortex is central for moral deficits in behavioural variant frontotemporal dementiaJournal of Neurology, Neurosurgery and Psychiatry, 86: 700-701.

Soda, P., Acciai, L., Cordelli, E., Costantini, I., Sacconi, L., Pavone, F.S., Conti, V., Guerrini, R., Frasconi, P., & Iannello, G. (2015). Computer-based automatic identification of neurons in gigavoxel-sized 3D human brain images37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): 7724-7727.

Sun, Z. Y., Pinel, P., Rivière, D., Moreno, A., Dehaene, S., & Mangin, J. F. (2015). Linking morphological and functional variability in hand movement and silent readingBrain Structure and Function, 1-11.

Tejedor, E., Becerra, Y., Alomar, G., Queralt, A., Badia, R.M., Torres, J., Cortes, T., & Labarta, J. (2015). PyCOMPS: parallel computation workflows in PythonInternational Journal of High Performance Computing Applications: doi: 10.1177/1094342015594678.

Thirion, B., Hoyos-Idrobo, A., Kahn, J., & Varoquaux, G. (2015). Fast clustering for scalable statistical analysis on structured images. ICML Workshop on Statistics, Machine Learning and Neuroscience.

Urgese, G., Barchi, F., & Macii, E. (2015). Top-down profiling of application specific Many-Core neuromorphic platforms2015 IEEE 9th International Symposium on Embedded Multi-Core(Many-Core Systems-on-Chip (MCSoC): 127-134.

Veltz, R., Chossat, P., & Faugeras, O. (2015). On the effects on cortical spontaneous activity of the symmetries of the network of pinwheels in visual area V1Journal of Mathematical Neuroscience, 5: 23.

Vidal, J.R., Perrone-Bertolotti, M., Kahane, P., & Lachaux, JP. (2015). Intracranial spectral amplitude dynamics of perceptual suppression in fronto-insular, occipito-temporal, and primary visual cortexFrontiers in Psychology, 5: 1545.

Wang, J., Yang, Y., Fan, L., Xu, J., Li, C., Liu, Y., Peter T. Fox, P. T., Eickhoff, S. B., Yu, C. & Jiang, T. (2015). Convergent functional architecture of the superior parietal lobule unraveled with multimodal neuroimaging approachesHuman Brain Mapping36: 238-257.

Wilf, M., Strappini, F., Golan, T., Hahamy, A., Harel, M., & Malach, R. (2015). Spontaneously emerging patterns in human visual cortex reflex responses to naturalistic sensory stimuliCerebral Cortex: doi: 10.1093/cercor/bhv275.

Xylouris, K., & Wittum, G. (2015). A three-dimensional mathematical model for the signal propagation on a neuron's membrane. Frontiers in Computational Neuroscience9: 94.

Zilles, K., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Friederici, A. D. (2015). Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprintsCortex63: 79-89.

41 HBP-funded scientific publications in 2014

Abdelmoula, W. M., Škrášková, K., Balluff, B., Carreira, R. J., Tolner, E. A., Lelieveldt, B. P., van der Maaten, L., Hans Morreau, H., van den Maagdenberg, A. M. J. M, Heeren, R. M. A., McDonnell, L. A. & Dijkstra, J. (2014). Automatic generic registration of mass spectrometry imaging data to histology using nonlinear stochastic embeddingAnalytical Chemistry86: 9204-9211.

Adhikari, P. R., Vavpetič, A., Kralj, J., Lavrač, N., & Hollmén, J. (2014, January). Explaining Mixture Models through Semantic Pattern Mining and Banded Matrix VisualizationDiscovery Science 8777: 1-12.

Anton-Sanchez, L., Bielza, C., Merchán-Pérez, A., Rodríguez, J. R., DeFelipe, J., & Larrañaga, P. (2014). Three-dimensional distribution of cortical synapses: a replicated point pattern-based analysisFrontiers in Neuroanatomy8: 1-15.

Antonelli, R., Pizzarelli, R., Pedroni, A., Fritschy, J. M., Del Sal, G., Cherubini, E., & Zacchi, P. (2014). Pin1-dependent signalling negatively affects GABAergic transmission by modulating neuroligin2/gephyrin interactionNature Communications5: 5066.

Asllani, M., Challenger, J. D., Pavone, F. S., Sacconi, L., & Fanelli, D. (2014). The theory of pattern formation on directed networksNature Communications5: 4517.

Bedard, C., & Destexhe, A. (2014). Generalized cable formalism to calculate the magnetic field of single neurons and neuronal populationsPhysical Review E, Statistical, Nonlinear, and Soft Matter Physics, 90: 042723. 

Bielza, C., & Larrañaga, P. (2014). Bayesian networks in neuroscience: a surveyFrontiers in Computational Neuroscience8: 131.

Bill, J., & Legenstein, R. (2014). A compound memristive synapse model for statistical learning through STDP in spiking neural networksFrontiers in Neuroscience8: 412.

Castellazzi, G., Palesi, F., Casali, S., Vitali, P., Sinforiani, E., Wheeler-Kingshott, C. A., & D'Angelo, E. (2014). A comprehensive assessment of resting state networks: bidirectional modification of functional integrity in cerebro-cerebellar networks in dementiaFrontiers in Neuroscience8: 223.

Chiovini, B., Turi, G. F., Katona, G., Kaszás, A., Pálfi, D., Maák, P., Gergely Szalay, G., Szabó, M. F., Szabó, G., Szadai Z., Káli, S. & Rózsa, B. (2014). Dendritic spikes induce ripples in parvalbumin interneurons during hippocampal sharp wavesNeuron82: 908-924.

Clarke, A. M., Herzog, M. H., & Francis, G. (2014). Visual crowding illustrates the inadequacy of local vs. global and feedforward vs. feedback distinctions in modeling visual perceptionFrontiers in Psychology5: 1193.

Corenthy, L., Garcia, M., Bayona, S., Santuy, A., San Martin, J., Benavides-Piccione, R., DeFelipe, J. & Pastor, L. (2014). Haptically Assisted Connection Procedure for the Reconstruction of Dendritic SpinesHaptics, IEEE Transactions on7: 486-498.

D'Angelo, E. (2014). The organization of plasticity in the cerebellar cortex: from synapses to controlProgress in Brain Research210: 31-58.

Frasconi, P., Silvestri, L., Soda, P., Cortini, R., Pavone, F. S., & Iannello, G. (2014). Large-scale automated identification of mouse brain cells in confocal light sheet microscopy imagesBioinformatics, 30: i587-i593.

Friedrich, P., Vella, M., Gulyás, A. I., Freund, T. F., & Káli, S. (2014). A flexible, interactive software tool for fitting the parameters of neuronal modelsFrontiers in Neuroinformatics8: 63.

Gandolfi, D., Pozzi, P., Tognolina, M., Chirico, G., Mapelli, J., & D'Angelo, E. (2014). The spatiotemporal organization of cerebellar network activity resolved by two-photon imaging of multiple single neuronsFrontiers in Cellular Neuroscience8: 92.

Giese, M. A. (2014). Skeleton model for the neurodynamics of visual action representationsArtificial Neural Networks and Machine Learning8681 :707-714.

Gudu, D., Hardt, M., & Streit, A. (2014). Evaluating the performance and scalability of the Ceph distributed storage system2014 IEEE International Conference onBig Data: 177-182.

Hernando, J. B., Duelo, C., & Martin, V. (2014). Visualization of Large-Scale Neural SimulationsBrain-Inspired Computing8603: 184-197.

Jonke, Z., Habenschuss, S., & Maass, W. (2014). A theoretical basis for efficient computations with noisy spiking neuronsarXiv preprint arXiv:1412.5862.

Kabdebon, C., Leroy, F., Simmonet, H., Perrot, M., Dubois, J., & Dehaene-Lambertz, G. (2014). Anatomical correlations of the international 10–20 sensor placement system in infantsNeuroImage99: 342-356.

King, J. R., & Dehaene, S. (2014). A model of subjective report and objective discrimination as categorical decisions in a vast representational space. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1641): 20130204.

Kunkel, S., Schmidt, M., Eppler, J. M., Plesser, H. E., Masumoto, G., Igarashi, J., Ishii, S., Fukai, T., Morrison, A., Diesmann, M. & Helias, M. (2014). Spiking network simulation code for petascale computersFrontiers in Neuroinformatics8: 78.

Legenstein, R., & Maass, W. (2014). Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environmentPLoS Computational Biology10(10): e1003859.

Maass, W. (2014). Noise as a resource for computation and learning in networks of spiking neuronsProceedings of the IEEE102(5): 860-880.

Mapelli, L., Solinas, S., & D'Angelo, E. (2014). Integration and regulation of glomerular inhibition in the cerebellar granular layer circuit. Frontiers in Cellular Neuroscience8: 55.

Mihaljević, B., Bielza, C., Benavides-Piccione, R., DeFelipe, J., & Larrañaga, P. (2014). Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty. Frontiers in Computational Neuroscience8: 150.

Monaco, J., Casellato, C., Koch, G., & D'Angelo, E. (2014). Cerebellar theta burst stimulation dissociates memory components in eyeblink classical conditioningEuropean Journal of Neuroscience40: 3363-3370.

Morales, J., Benavides-Piccione, R., Dar, M., Fernaud, I., Rodríguez, A., Anton-Sanchez, L., Bielza, C., Larrañaga, P., DeFelipe, J. & Yuste, R. (2014). Random Positions of Dendritic Spines in Human Cerebral CortexThe Journal of Neuroscience34: 10078-10084.

Nachbaur, D., Dumusc, R., Bilgili, A., Hernando, J., & Eilemann, S. (2015). Remote parallel rendering for high-resolution tiled display walls. Proceedings of IEEE Symposium on Large Data Analysis and Visualization. 

Naud, R., Bathellier, B., & Gerstner, W. (2014). Spike-timing prediction in cortical neurons with active dendritesFrontiers in Computational Neuroscience,8: 90.

Nieus, T. R., Mapelli, L., & D'Angelo, E. (2014). Regulation of output spike patterns by phasic inhibition in cerebellar granule cellsFrontiers in cellular neuroscience8: 246.

Pecevski, D., Kappel, D., & Jonke, Z. (2014). NEVESIM: event-driven neural simulation framework with a Python interface. Frontiers in Neuroinformatics8: 70.

Raya, L., Bayona, S., Pastor, L., & Garcia, M. (2014). A new user-adapted search haptic algorithm to navigate along filiform structuresIEEE Transactions on Haptics7(3): 273-284.

Robnik-Šikonja, M. (2014). Generator of unsupervised semi-artificial data. University of Lublijana, Faculty of Computer and Information Science.

Schlingloff, D., Káli, S., Freund, T. F., Hájos, N., & Gulyás, A. I. (2014). Mechanisms of sharp wave initiation and ripple generationThe Journal of Neuroscience34(34): 11385-11398.

Schuller, B., Benedyczak, K., & Rybicki, J. (2014). High-Performance Computing on the Web: Extending UNICORE with RESTful Interfaces. In The Sixth International Conference on Advances in Future Internet (No. FZJ-2014-06228). Jülich Supercomputing Center.

Subramaniyam, S., Solinas, S., Perin, P., Locatelli, F., Masetto, S., & D'Angelo, E. (2014). Computational modeling predicts the ionic mechanism of late-onset responses in unipolar brush cellsFrontiers in Cellular Neuroscience8: 237.

Testa-Silva, G., Verhoog, B., Linaro, D., de Kock, C.P., Baayen, J.C., Meredith, R.M., De Zeeuw, C.I., Giugliano, M., & Mansvelder, H.D. (2014). High bandwidth synaptic communication and frequency tracking in human neocortexPLoS Biology, 12: e1002007.

Van Der Maaten, L. (2014). Accelerating t-SNE using tree-based algorithmsThe Journal of Machine Learning Research15(1), 3221-3245.

Velychko, D., Endres, D., Taubert, N., & Giese, M. A. (2014). Coupling Gaussian Process Dynamical Models with Product-of-Experts Kernels. Artificial Neural Networks and Machine Learning–ICANN 20148681: 603-610.


Other relevant publications

Amunts, K., Lindner, A., & Zilles, K. (2014). The human brain project: neuroscience perspectives and German contributionse-Neuroforum5(2): 43-50.

Amunts, K., Zilles, K., & Lindner, A. (2014). The Human Brain Project: Neurowissenschaftliche Perspektiven und Beiträge aus Deutschland. Neuroforum2(14): 222-229.

Barry, C., & Burgess, N. (2014). Neural mechanisms of self-locationCurrent Biology24: R330-R339.

Bédard, C., & Destexhe, A. (2014). Mean-Field Formulation of Maxwell Equations to Model Electrically Inhomogeneous and Isotropic Media. Journal of Electromagnetic Analysis and Applications6: 296-302.

Ben-Yakov, A., Rubinson, M., & Dudai, Y. (2014). Shifting gears in hippocampus: temporal dissociation between familiarity and novel signatures in a single eventJournal of Neuroscience, 34: 12973-12981.

Bielza, C., Benavides-Piccione, R., López-Cruz, P., Larranaga, P., & DeFelipe, J. (2014). Branching angles of pyramidal cell dendrites follow common geometrical design principles in different cortical areasScientific Reports4: 5909.

Bush, D., & Burgess, N. (2014). A hybrid oscillatory interference/continuous attractor network model of grid cell firing. The Journal of Neuroscience34(14): 5065-5079.

Bush, D., Barry, C., & Burgess, N. (2014). What do grid cells contribute to place cell firing? Trends in Neurosciences37: 136-145.

Cabral, J., Kringelbach, M. L., & Deco, G. (2014). Exploring the network dynamics underlying brain activity during restProgress in Neurobiology114: 102-131.

Cabral, J., Luckhoo, H., Woolrich, M., Joensson, M., Mohseni, H., Baker, A., Kringelbach, M. L. & Deco, G. (2014). Exploring mechanisms of spontaneous functional connectivity in MEG: how delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillationsNeuroimage90, 423-435.

Caspers, J., Zilles, K., Amunts, K., Laird, A.R., Fox, P.T., & Eickhoff, S.B. (2014). Functional characterization and differential coactivation patterns of two cytoarchitectonic visual areas on the human posterior fusiform gyrusHuman Brain Mapping, 35: 2754-2767.

Caspers, J., Zilles, K., Beierle, C., Rottschy, C., & Eickhoff, S.B. (2014). A novel meta-analytic approach: mining frequent co-activation patterns in neuroimaging databasesNeuroimage, 90: 390-402.

Deco, G., & Kringelbach, M. L. (2014). Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron84: 892-905.

Deco, G., Ponce-Alvarez, A., Hagmann, P., Romani, G. L., Mantini, D., & Corbetta, M. (2014). How Local Excitation–Inhibition Ratio Impacts the Whole Brain DynamicsThe Journal of Neuroscience34: 7886-7898.

Dehaene, S., Charles, L., King, J. R., & Marti, S. (2014). Toward a computational theory of conscious processingCurrent Opinion in Neurobiology, 25: 76-84.

Dohmatob, E. D., Gramfort, A., Thirion, B., & Varoquaux, G. (2014). Benchmarking solvers for TV-ℓ 1 least-squares and logistic regression in brain imaging2014 International Workshop on Pattern Recognition in Neuroimaging: 1-4.

Doolittle, E. L., Gingras, B., Endres, D. M., & Fitch, W. T. (2014). Overtone-based pitch selection in hermit thrush song: Unexpected convergence with scale construction in human musicProceedings of the National Academy of Sciences111: 16616-16621.

Dudai, Y., & Evers, K. (2014). To Simulate or Not to Simulate: What Are the Questions? Neuron84(2): 254-261.

Edelson, M. G., Dudai, Y., Dolan, R. J., & Sharot, T. (2014). Brain substrates of recovery from misleading influenceThe Journal of Neuroscience34: 7744-7753.

Frangeul, L., Porrero, C., Garcia-Amado, M., Maimone, B., Maniglier, M., Clascá, F., & Jabaoudon, D. (2014). Specific activation of the paralemniscal pathway during nociceptionEuropean Journal of Neuroscience, 39: 1455-1464.

Gabitov, E., Manor, D., & Karni, A. Done that: short-term repetition-related modulations of motor cortex activity as a stable signature for overnight motor memory consolidationJournal of Cognitive Neuroscience, 26: 2716-2734.

Galili, T., Mitelpunkt, A., Shachar, N., Marcus-Kalish, M., & Benjamini, Y. (2014). Categorize, Cluster, and Classify: A 3-C Strategy for Scientific Discovery in the Medical Informatics Platform of the Human Brain ProjectDiscovery Science8777: 73-86.

Galili, T., Mitelpunkt, A., Shachar, N., Marcus-Kalish, M., & Benjamini, Y. (2014, January). Erratum: Categorize, Cluster, and Classify: A 3-C Strategy for Scientific Discovery in the Medical Informatics Platform of the Human Brain ProjectDiscovery Science, 8777: E1-E1.

Gutierrez-Arenas, O., Eriksson, O., & Hellgren Kotaleski, J. (2014). Segregation and Crosstalk of D1 Receptor-Mediated Activation of ERK in Striatal Medium Spiny Neurons upon Acute Administration of PsychostimulantsPLoS Computational Biology10: e1003445.

Hänel, C., Pieperhoff, P., Hentschel, B., Amunts, K., & Kuhlen, T. (2014). Interactive 3D visualization of structural changes in the brain of a person with corticobasal syndromeFrontiers in Neuroinformatics8: 42.

Kandel, E. R., Dudai, Y., & Mayford, M. R. (2014). The molecular and systems biology of memoryCell157(1): 163-186.

Langner, R., Rottschy, C., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2014). Meta-analytic connectivity modeling revisited: Controlling for activation base ratesNeuroimage99: 559-570.

Lorio, S., Lutti, A., Kherif, F., Ruef, A., Dukart, J., Chowdhury, R., Frackowiak, R.S., Ashburner, J., Helms, G., Weiskopf, N., & Draganski, B. (2014). Disentangling in vivo the effects of iron content and atrophy on the ageing human brainNeuroimage, 103: 280-289.

Meli, G., Lecci, A., Manca, A., Krako, N., Albertini, V., Benussi, L., ... & Cattaneo, A. (2014). Conformational targeting of intracellular Aβ oligomers demonstrates their pathological oligomerization inside the endoplasmic reticulumNature Communications5: 3867.

Mihai, P. G., Otto, M., Platz, T., & Eickhoff, S. B. (2014). Sequential evolution of cortical activity and effective connectivity of swallowing using fMRIHuman Brain Mapping35: 5962-5973.

Nettekoven, C., Volz, L. J., Kutscha, M., Pool, E. M., Rehme, A. K., Eickhoff, S. B., ... & Grefkes, C. (2014). Dose-dependent effects of theta burst rTMS on cortical excitability and resting-state connectivity of the human motor system.The Journal of Neuroscience34: 6849-6859.

Papp, E. A., Leergaard, T. B., Calabrese, E., Johnson, G. A., & Bjaalie, J. G. (2014). Waxholm Space atlas of the Sprague Dawley rat brainNeuroimage, 97: 374-386.

Robertson, B., Kardamakis, A., Capantini, L., Pérez-Fernández, J., Suryanarayana, S.M., Wallén, P., Stephenson-Jones, M., & Grillner S. (2014). The lamprey blueprint of the mammalian nervous systemProgress in Brain Research, 212: 337-349.

Romos-Moreno, T., & Clascá, F. (2014). Quantitative mapping of the local and extrinsic sources of GABA and Reelin to the layer Ia neuropil in the adult rat neocortex.  Brain Structure and Function, 219: 1639-1657.

Salles, A, & Evers, K. (2014). La Vida Social del Cerebro. Derecho, Salud y Bioética.

Steratt, D.C., Sorokina, O., & Armstrong, D.J. (2014). Integration of rule-based models and compartmental models of neuronsThird International Workshop on Hybrid Systems Biology, Vienna, July 23-24, 2014.

Tauheed, F., Heinis, T., Schurmann, F., Markram, H., & Ailamaki, A. (2014). OCTOPUS: Efficient query execution on dynamic mesh datasets2014 IEEE 30th International Conference on Data Engineering (ICDE): 1000-1011.

Thirion, B., Varoquaux, G., Grisel, O., Poupon, C., & Pinel, P. (2014). Principal Component Regression predicts functional responses across individualsMedical Image Computing and Computer-Assisted Intervention 8674: 741-748.

Toharia, P., Morales, J., de Juan, O., Fernaud, I., Rodríguez, A., & DeFelipe, J. (2014). Musical Representation of Dendritic Spine Distribution: A New Exploratory ToolNeuroinformatics12: 341-353.

Vandoorne, K., Mechet, P., Van Vaerenbergh, T., Fiers, M., Morthier, G., Verstraeten, D., Schrauwen, B., Dambre, J. & Bienstman, P. (2014). Experimental demonstration of reservoir computing on a silicon photonics chipNature Communications5: 3541.

Weiner, K.S., Golarai, G., Caspers, J., Chuapoco, M.R., Mohlberg, H., Zilles, K., Amunts, K., & Grill-Spector, K. (2014). The mid-fusiform sulcus: a landmark identifying both cytoarchitectonic and functional divisions of human ventral temporal cortexNeuroimage, 84: 453-465.