Blue Brain Portal

Resources

The Blue Brain Portal is a newly created knowledge space for neuroscientists.  EPFL's Blue Brain Project recognizes that knowledge sharing is an important driving force to consolidate and promote simulation neuroscience which in turn, is fundamental to understanding the brain as a complex multi-scale system. Therefore, the Blue Brain Portal brings together in one place open-sourced software, tools, models and data, both from us and our collaborators. The aim is for this knowledge to be utilized by both the neuroscientific and the wider scientific community to develop the field of simulation neuroscience. Join Blue Brain’s journey to simulate the brain.  

Models

Multi-scale models of the rat and mouse brain integrate models of ion channels, single cells, microcircuits, brain regions, and brain systems at different levels of granularity (molecular models, morphologically detailed cellular models, and abstracted point neuron models).

Online Tools

The online tools available to assist users with simulation neuroscience are a collection of platforms and atlases from the Blue Brain and our collaborators.

Online Learning

Simulation Neuroscience is an emerging approach to integrate the knowledge dispersed throughout the field of neuroscience.

Software

Blue Brain’s reconstruction and related simulations are made possible by a comprehensive software ecosystem for each step in the reconstruction and simulation process. Blue Brain systematically releases open source software.

Data

Data is of vital importance to the study of the Brain.  For neuroscientists in this section, there are ion channel recordings, morphological reconstructions, electrical recordings from neocorticol neurons and molecular properties of neurons.  These are available on Blue Brain platforms and the Human Brain Project Brain Simulation platform.

Publications

PublicationYearAuthors
1512019Fan X and Markram H (2019) A Brief History of Simulation Neuroscience. Front. Neuroinform. 13:32. 07 May 2019 doi.org/10.3389/fninf.2019.00032
1502019Kumbhar, P., Awile, O., Keegan, L., Blanco Alonso, J., King, J., Hines, M., and Schürmann, F. (2019). An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language. 6 May 2019. arXiv:1905.02241.
1492019Colangelo, C., Shichkova, P., Keller, D., Markram, H., and Ramaswamy, S. (2019). Cellular, Synaptic and Network Effects of Acetylcholine in the Neocortex. Frontiers in Neural Circuits 13, 24. https://doi.org/10.3389/fncir.2019.00024
1482019Kanari, L., Ramaswamy, S., Shi, Y., Morand, S., Meystre, Julie., Perin, R., Abdellah, M., Wang, Y., Hess, K., Markram., Objective Morphological Classification of Neocortical Pyramidal Cells, Cerebral Cortex, Volume 29, Issue 4, April 2019, Pages 1719–1735, https://doi.org/10.1093/cercor/bhy339
1472019Barros-Zulaica, N., Villa, A.E.P., and Nuñez, A. (2019). Response adaptation in barrel cortical neurons facilitates stimulus detection during rhythmic whisker stimulation in anesthetized mice. eNeuro 6: 2. ENEURO.0471-18.2019. 25 March 2019. https://doi.org/10.1523/ENEURO.0471-18.2019.
1462019Muddapu V.R., Mandali A., Chakravarthy V.S., and Ramaswamy S. (2019). A Computational Model of Loss of Dopaminergic Cells in Parkinson’s Disease Due to Glutamate-Induced Excitotoxicity. Front. Neural Circuits 13:11. https://doi.org/10.3389/fncir.2019.00011.
1452019Reimann, M.W., Gevaert, M., Shi, Y., Lu, H., Markram, H., and Muller, E. (2019). A null model of the mouse whole-neocortex micro-connectome. bioRxiv. 13 February 2019. https://doi.org/10.1101/548735.
1442019Kumbhar, P., Hines, M., and Schürmann, F. (2019). CoreNEURON – An Optimized Compute Engine of NEURON Simulator. arXiv. 30 January 2019. https://arxiv.org/abs/1901.10975v.
1432019Cremonesi, F., Hager, G., Wellein, G., and Schürmann, F. (2019). Analytical Performance Modelling of Detailed Brain Simulations. arXiv. 16 January 2019. http://arxiv.org/abs/1901.05344.
1422019Iavarone, E., Yi, J., Shi, Y., Zandt, B.-J., O’Reilly, C., Van Geit, W., Rossert, C., Markram, H., and Hill, S.L. (2019). Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons. bioRxiv 4 January 2019. https://www.biorxiv.org/content/10.1101/512269v3.
1412018Planas, J., Delalondre, F., and Schürmann, F. (2018). Accelerating Data Analysis in Simulation Neuroscience with Big Data Technologies. In Computational Science – ICCS 2018, Y. Shi, et al., eds. (Springer International Publishing), Lecture Notes in Computer Science book series (LNCS, volume 10860), 363–377. https://www.springerprofessional.de/en/accelerating-data-analysis-in-simulation-neuroscience-with-big-d/15836908
1402018Abdellah, M., Hernando, J., Eilemann, S., Lapere, S., Antille, N., Markram, H., and Schürmann, F. (2018). NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks. Bioinformatics 34, i574–i582. https://doi.org/10.1093/bioinformatics/bty231.
1392018Coggan, J.S., Calì, C., Keller, D., Agus, M., Boges, D., Abdellah, M., Kare, K., Lehväslaiho, H., Eilemann, S., Jolivet, R.B., Hadwiger, M., Markram, H., Schürmann, F., Magistretti, P.J. (2018a). A Process for Digitizing and Simulating Biologically Realistic Oligocellular Networks Demonstrated for the Neuro-Glio-Vascular Ensemble. Frontiers in Neuroscience 12, 664. https://doi.org/10.3389/fnins.2018.00664.
1382018Coggan, J.S., Keller, D., Calì, C., Lehväslaiho, H., Markram, H., Schürmann, F., and Magistretti, P.J. (2018b). Norepinephrine stimulates glycogenolysis in astrocytes to fuel neurons with lactate. PLOS Computational Biology 14 (8). https://doi.org/10.1371/journal.pcbi.1006392.
1372018Erö, C., Gewaltig, M.-O., Keller, D., and Markram, H. (2018). A Cell Atlas for the Mouse Brain. Frontiers in Neuroinformatics 12, 84. . https://doi.org/10.3389/fninf.2018.00084.
1362018Eyal, G., Verhoog, M.B., Testa-Silva, G., Deitcher, Y., Benavides-Piccione, R., DeFelipe, J., de Kock, C.P.J., Mansvelder, H.D., and Segev, I. (2018). Human Cortical Pyramidal Neurons: From Spines to Spikes via Models. Frontiers in Cellular Neuroscience 12, 181. https://doi.org/10.3389/fncel.2018.00181.
13652018Johannes, L., Pezeshkian, W., Ipsen, J.H., and Shillcock, J.C. (2018). Clustering on Membranes: Fluctuations and More. Trends in Cell Biology 28, 405–415.
https://www.ncbi.nlm.nih.gov/pubmed/29502867.
1342018Kanari, L., Dłotko, P., Scolamiero, M., Levi, R., Shillcock, J., Hess, K., and Markram, H. (2018a). A Topological Representation of Branching Neuronal Morphologies. Neuroinformatics 16, 3–13. https://link.springer.com/article/10.1007%2Fs12021-017-9341-1.
1332018Keller, D., Erö, C., and Markram, H. (2018). Cell Densities in the Mouse Brain: A Systematic Review. Front. Neuroanat. 12, 83. https://doi.org/10.3389/fnana.2018.00083.
1322018Kupper, R., Schmuker, M., Aertsen, A., Wachtler, T., Körner, U., and Gewaltig, M.-O. (2018). How the visual system can detect feature homogeneity from spike latencies. arXiv. https://arxiv.org/abs/1806.03881.
1312018Lindroos, R., Dorst, M.C., Du, K., Filipović, M., Keller, D., Ketzef, M., Kozlov, A.K., Kumar, A., Lindahl, M., Nair, A.G., Pérez-Fernández, J., Grillner, S., Silberberg, G., Hellgren Kotaleski, J. (2018). Basal ganglia neuromodulation over multiple temporal and structural scales-simulations of direct pathway MSNs investigate the fast onset of dopaminergic effects and predict the role of Kv4.2. Front Neural Circuits 12, 3. https://doi.org/10.3389/fncir.2018.00003.
1302018Migliore, R., Lupascu, C.A., Bologna, L.L., Romani, A., Courcol, J.-D., Antonel, S., Van Geit, W.A.H., Thomson, A.M., Mercer, A., Lange, S., Falck, J., Roessert, C. A., Freund, T. F., Kali, S., Muller, E. B., Schürmann, F., Markram, H., Migliore, M. (2018). The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow. PLOS Computational Biology 14 (9). https://doi.org/10.1371/journal.pcbi.1006423.
1292018Nolte, M., Reimann, M.W., King, J.G., Markram, H., and Muller, E.B. (2018). Cortical Reliability Amid Noise and Chaos. bioRxiv, January 1, 2018. https://doi.org/10.1101/304121.
1282018O’Reilly C., Chapotot F., Pittau F., Mella N., Picard F. (2018, accepted for publication). Nicotine increases sleep spindle activity. Journal of Sleep Research.
1272018Pezeshkian, W., Gao, H., Arumugam, S., Becken, U., Bassereau, P., Florent, J.-C., Ipsen, J.H., Johannes, L., and Shillcock, J.C. (2018). Mechanism of Shiga Toxin Clustering on Membranes. ACS Nano 12, 2079–2079.
https://pubs.acs.org/doi/10.1021/acsnano.8b00537.
1262018Ramaswamy, S., Colangelo, C., and Markram, H. (2018). Data-Driven Modeling of Cholinergic Modulation of Neural Microcircuits: Bridging Neurons, Synapses and Network Activity. Front. Neural Circuits 12, 77–77. https://doi.org/10.3389/fnana.2018.00083.
1252018Ramaswamy, S., Muller, E., Reimann, M., and Markram, H. (2018). Microcircuitry of the neocortex. In Handbook of Brain Microcircuits, Section 1: Neocortex, Chapter 3, G.M. Shepherd, and S. Grillner, eds. (Oxford University Press), p. 35-46. https://books.google.ch/books?id=n8M9DwAAQBAJ.
1242018Shardlow, M., Ju, M., Li, M., O’Reilly, C., Iavarone, E., McNaught, J., and Ananiadou, S. (2018). A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience. Neuroinformatics. 15 Nov 2018, 1-6. https://doi.org/10.1007/s12021-018-9404-y.
1232018Wybo, W., Torben-Nielsen, B., and Gewaltig, M.-O. (2018). Dynamic compartmentalization in neurons enables branch specific learning. bioRxiv. https://www.biorxiv.org/content/early/2018/01/08/244772.
1222017Abdellah, M., Bilgili, A., Eilemann, S., Markram, H., and Schürmann, F. (2017). A Physically Plausible Model for Rendering Highly Scattering Fluorescent Participating Media. arXiv, v2, 12 June 2017. https://arxiv.org/abs/1706.03024v2.
1212017Abdellah, M., Bilgili, A., Eilemann, S., Shillcock, J., Markram, H., and Schürmann, F. (2017). Bio-physically plausible visualization of highly scattering fluorescent neocortical models for in silico experimentation. BMC Bioinformatics 18, 62.
1202017Abdellah, M., Hernando, J., Antille, N., Eilemann, S., Markram, H., and Schürmann, F. (2017). Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies. BMC Bioinformatics 18, 402.
1192017Deitcher, Y., Eyal, G., Kanari, L., Verhoog, M.B., Atenekeng Kahou, G.A., Mansvelder, H.D., de Kock, C.P.J., and Segev, I. (2017). Comprehensive Morpho-Electrotonic Analysis Shows 2 Distinct Classes of L2 and L3 Pyramidal Neurons in Human Temporal Cortex. Cereb. Cortex 27, 5398–5414.
1182017Doron, M., Chindemi, G., Muller, E., Markram, H., and Segev, I. (2017). Timed Synaptic Inhibition Shapes NMDA Spikes, Influencing Local Dendritic Processing and Global I/O Properties of Cortical Neurons. Cell Rep 21, 1550–1561.
1172017Eilemann, S., Abdellah, M., Antille, N., Bilgili, A., Chevtchenko, G., Dumusc, R., Favreau, C., Hernando, J., Nachbaur, D., Podhajski, P., Villafranca, J., Schürmann, F. (2017). From Big Data to Big Displays High-Performance Visualization at Blue Brain. In High Performance Computing, J.M. Kunkel, R. Yokota, M. Taufer, and J. Shalf, eds. (Springer International Publishing), pp. 662–675.
1162017Ewart, T., Planas, J., Cremonesi, F., Langen, K., Schürmann, F., and Delalondre, F. (2017). Neuromapp: A Mini-application Framework to Improve Neural Simulators. In High Performance Computing, J.M. Kunkel, R. Yokota, P. Balaji, and D. Keyes, eds. (Springer International Publishing), pp. 181–198.
1152017Falotico, E., Vannucci, L., Ambrosano, A., Albanese, U., Ulbrich, S., Vasquez Tieck, J.C.,
Hinkel, G., Kaiser, J., Peric, I., Denninger, O., Cauli, N., Kirtay, M., Roennau, A., Klinker, G.,
Von Arnim, A., Guyot, L., Peppicelli, D., Martínez-Cañada, P., Ros, E., Maier, P., Weber, S., Huber, M., Plecher, D., Röhrbein, F., Deser, S., Roitberg, A., van der Smagt, P., Dillman, R., Levi, P., Laschi, C., Knoll, A.C., Gewaltig, M.-O. (2017). Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform. Front Neurorobot 11, 2.
1142017Gal, E., London, M., Globerson, A., Ramaswamy, S., Reimann, M.W., Muller, E., Markram, H., and Segev, I. (2017). Rich cell-type-specific network topology in neocortical microcircuitry. Nat. Neurosci. 20, 1004–1013.
1132017Hinkel, G., Groenda, H., Krach, S., Vannucci, L., Denninger, O., Cauli, N., Ulbrich, S., Roennau, A., Falotico, E., Gewaltig, M.-O., Knoll, A., Dillmann, R., Laschi, C., Reussner, R. (2017). A framework for coupled simulations of robots and spiking neuronal networks. Journal of Intelligent & Robotic Systems 85, 71–91. https://link.springer.com/article/10.1007/s10846-016-0412-6.
1122017Masoli, S., Rizza, M.F., Sgritta, M., Van Geit, W., Schürmann, F., and D’Angelo, E. (2017). Single Neuron Optimization as a Basis for Accurate Biophysical Modeling: The Case of Cerebellar Granule Cells. Front Cell Neurosci 11, 71.
1112017O’Reilly, C., Iavarone, E., and Hill, S.L. (2017). A Framework for Collaborative Curation of Neuroscientific Literature. Front Neuroinform 11, 27.
1102017Podlaski, W.F., Seeholzer, A., Groschner, L.N., Miesenböck, G., Ranjan, R., and Vogels, T.P. (2017). Mapping the function of neuronal ion channels in model and experiment. Elife 6.
1092017Ramaswamy, S., Colangelo, C., and Muller, E.B. (2017). Distinct Activity Profiles of Somatostatin-Expressing Interneurons in the Neocortex. Front Cell Neurosci 11, 273.
1082017Reimann, M.W., Horlemann, A.-L., Ramaswamy, S., Muller, E.B., and Markram, H. (2017). Morphological Diversity Strongly Constrains Synaptic Connectivity and Plasticity. Cereb. Cortex 27, 4570–4585.
1072017Reimann, M.W., Nolte, M., Scolamiero, M., Turner, K., Perin, R., Chindemi, G., Dłotko, P., Levi, R., Hess, K., and Markram, H. (2017). Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function. Front Comput Neurosci 11, 48.
1062017Rössert, C., Pozzorini, C., Chindemi, G., Davison, A.P., Eroe, C., King, J., Newton, T.H., Nolte, M., Ramaswamy, S., Reimann, M.W., et al. (2017). Automated point-neuron simplification of data-driven microcircuit models. Version 2, 30 March 2017. arXiv. https://arxiv.org/abs/1604.00087.
1052017Schumann, T., Erő, C., Gewaltig, M.-O., and Jonathan Delalondre, F. (2017). Towards Simulating Data-Driven Brain Models at the Point Neuron Level on Petascale Computers. In High-Performance Scientific Computing: First JARA-HPC Symposium, JHPCS 2016, October 4–5, 2016, Revised Selected Papers, Di Napoli, E. et al., Eds (Aachen, Germany: Springer International Publishing), pp. 160–169. https://books.google.ch/books?id=bB89DgAAQBAJ
1042016Ambrosano, A., Vannucci, L., Albanese, U., Kirtay, M., Falotico, E., Martínez-Cañada, P., Hinkel, G., Kaiser, J., Ulbrich, S., Levi, P., Morillas, C., Knoll, A., Gewaltig, M-O., Laschi, C. (2016). Retina Color-Opponency Based Pursuit Implemented Through Spiking Neural Networks in the Neurorobotics Platform. In Biomimetic and Biohybrid Systems, N.F. Lepora, A. Mura, M. Mangan, P.F.M.J. Verschure, M. Desmulliez, and T.J. Prescott, eds. (Springer International Publishing), pp. 16–27. https://link.springer.com/chapter/10.1007%2F978-3-319-42417-0_2.
1032016Amsalem, O., Van Geit, W., Muller, E., Markram, H., and Segev, I. (2016). From Neuron Biophysics to Orientation Selectivity in Electrically Coupled Networks of Neocortical L2/3 Large Basket Cells. Cereb. Cortex 26, 3655–3668.
1022016Eilemann, S., Delalondre, F., Bernard, J., Planas, J., Schuermann, F., Biddiscombe, J., Bekas, C., Curioni, A., Metzler, B., Kaltstein, P., Morjan, P., Fenkes, J., Bellofatto, R., Schneidenbach, L., Ward, T.J.C., Fitch, B.G. (2016). Key/Value-Enabled Flash Memory for Complex Scientific Workflows with On-Line Analysis and Visualization. In IEEE International Parallel and Distributed Processing Symposium (IPDPS), (IEEE), pp. 608–617.
1012016Eyal G., Verhoog M. B., Testa-Silva G., Deitcher Y., Lodder J. C., Benavides-Piccione R., Morales J., DeFelipe J., de Kock C. P.J., Mansvelder H. D., Segev I. (2016). Unique
membrane properties and enhanced signal processing in human neocortical neurons. eLife 6;5. pii: e16553.
1002016Halnes, G., Mäki-Marttunen, T., Keller, D., Pettersen, K.H., Andreassen, O.A., and Einevoll, G.T. (2016). Effect of Ionic Diffusion on Extracellular Potentials in Neural Tissue. PLoS Comput. Biol. 12, e1005193.
992016Hill S., How do we know what we know? Discovering neuroscience data sets through minimal metadata (2016). Nat Rev Neurosci 12, 735.
982016Kanari, L., Dlotko, P., Scolamiero, M., Levi, R., Shillcock, J., Hess, K., and Markram, H. (2016). Quantifying topological invariants of neuronal morphologies. arXiv.
972016Knoll A., Gewaltig M-O., Neurorobotics: A strategic pillar of the Human Brain Project (2016). Chapter in Supplement to Science. Robotics in Brain- inspired intelligent robotics: The intersection of robotics and neuroscience (Science/AAAS, Washington, DC, 2016), p. 25-34.
962016Kumbhar, P., Hines, M., Ovcharenko, A., Mallon, D.A., King, J., Sainz, F., Schürmann, F., and Delalondre, F. (2016). Leveraging a Cluster-Booster Architecture for Brain-Scale Simulations. In High Performance Computing, J.M. Kunkel, P. Balaji, and J. Dongarra,
eds. (Springer International Publishing), pp. 363–380.
952016Leitner, F., Bielza, C., Hill, S.L., and Larrañaga, P. (2016). Data Publications Correlate with Citation Impact. Front Neurosci 10, 419.
942016Lytton, W.W., Seidenstein, A.H., Dura-Bernal, S., McDougal, R.A., Schürmann, F., and Hines, M.L. (2016). Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON. Neural Comput 28, 2063–2090.
932016Magalhães, B.R.C., Tauheed, F., Heinis, T., Ailamaki, A., and Schürmann, F. (2016). An Efficient Parallel Load-Balancing Framework for Orthogonal Decomposition of Geometrical Data. In: Kunkel J., Balaji P., Dongarra J. (eds) High Performance Computing. Lecture
Notes in Computer Science,. In High Performance Computing, J.M. Kunkel, P. Balaji, and J. Dongarra, eds. (Springer International Publishing), pp. 81–97.
922016Roehrbein, F., Gewaltig, M.-O., Laschi, C., Klinker, G., Levi, P., and Knoll, A. (2016). The Neurorobotic Platform: A simulation environment for brain-inspired robotics. In ISR 2016: 47st International Symposium on Robotics; Proceedings Of, (VDE), pp. 1–6. Shillcock, J.C. (2012). Spontaneous Vesicle Self-Assembly: A Mesoscopic View of Membrane Dynamics. Langmuir 28, 541-547. https://ieeexplore.ieee.org/document/7559143.
912016Shillcock, J.C., Hawrylycz, M., Hill, S., and Peng, H. (2016). Reconstructing the brain: from image stacks to neuron synthesis. Brain Inform 3, 205–209.
902016Van Geit, W., Gevaert, M., Chindemi, G., Rössert, C., Courcol, J.-D., Muller, E.B., Schürmann, F., Segev, I., and Markram, H. (2016). BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience. Front Neuroinform 10.
892016Vanherpe, L., Kanari, L., Atenekeng, G., Palacios, J., and Shillcock, J. (2016). Framework for efficient synthesis of spatially embedded morphologies. Phys Rev E 94, 023315.
882016Vanherpe, L., Kanari, L., Atenekeng, G., Palacios, J., and Shillcock, J. (2016). In situ synthesis and simulation of polydisperse amphiphilic membranes. International Journal of Advances in Engineering Sciences and Applied Mathematics 8, 126–133.
872016Wang, Q., Abdul, S., Almeida, L., Ananiadou, S., Balderas-Martínez, Y.I., Batista-Navarro, R., Campos, D., Chilton, L., Chou, H.-J., Contreras, G., Cooper, L., Dai, H.-J., Ferrell, B., Fluck, J., Gama-Castro, S., George, N., Gkoutos, G., Irin, A.K., Jensen, L.J., Jimenez, S., Jue, T.R., Keseler, I., Madan, S., Matos, S., McQuilton, P., Milacic, M., Mort, M., Natarajan, J., Pafilis, E., Pereira, E., Rao, S., Rinaldi, F., Rothfels, K., Salgado, D., Silva, R.M., Singh, O., Stefancsik, R., Su, C.-H., Subramani, S., Tadepally, H.D., Tsaprouni, L., Vasilevsky, N., Wang, X., Chatr-Aryamontri, A., Laulederkind, S.J.F., Matis-Mitchell, S., McEntyre, J., Orchard, S., Pundir, S., Rodriguez-Esteban, R., Van Auken, K., Lu, Z., Schaeffer, M., Wu, C.H., Hirschman, L., Arighi, C.N. (2016). Overview of the interactive task in BioCreative V. Database: The Journal of Biological Databases and Curation (Oxford), Volume 2016, 1 January 2016, https://doi.org/10.1093/database/baw119.
862015H. Markram, E. Muller, S. Ramaswamy, Michael W. Reimann, M. Abdellah, Carlos A. Sanchez, A. Ailamaki, L. Alonso-Nanclares, N. Antille, S. Arsever, Guy Antoine A. Kahou, Thomas K. Berger, A. Bilgili, N. Buncic, A. Chalimourda, G. Chindemi, J.-D. Courcol, F. Delalondre, V. Delattre, S. Druckmann, R. Dumusc, J. Dynes, S. Eilemann, E. Gal, Michael E. Gevaert, J.-P. Ghobril, A. Gidon, Joe W. Graham, A. Gupta, V. Haenel, E. Hay, T. Heinis, Juan B. Hernando, M. Hines, L. Kanari, D. Keller, J. Kenyon, G. Khazen, Y. Kim, James G. King, Z. Kisvarday, P. Kumbhar, S. Lasserre, J.-V. Le Bé, Bruno R.C. Magalhães, A. Merchán-Pérez, J. Meystre, Benjamin R. Morrice, J. Muller, A. Muñoz-Céspedes, S. Muralidhar, K. Muthurasa, D. Nachbaur, Taylor H. Newton, M. Nolte, A. Ovcharenko, J. Palacios, L. Pastor, R. Perin, R. Ranjan, I. Riachi, J.-R. Rodríguez, Juan L. Riquelme, C. Rössert, K. Sfyrakis, Y. Shi, Julian C. Shillcock, G. Silberberg, R. Silva, F. Tauheed, M. Telefont, M. Toledo-Rodriguez, T. Tränkler, W. Van Geit, Jafet V. Díaz, R. Walker, Y. Wang, Stefano M. Zaninetta, J. DeFelipe, Sean L. Hill, I. Segev, and F. Schürmann, Reconstruction and Simulation of Neocortical Microcircuitry. Cell 163, 2015, 456-492.
852015S. Ramaswamy, J.-D. Courcol, M. Abdellah, S.R. Adaszewski, N. Antille, S. Arsever, G. Atenekeng, A. Bilgili, Y. Brukau, A. Chalimourda, G. Chindemi, F. Delalondre, R. Dumusc, S. Eilemann, M.E. Gevaert, P. Gleeson, J.W. Graham, J.B. Hernando, L. Kanari, Y. Katkov, D. Keller, J.G. King, R. Ranjan, M.W. Reimann, C. Rössert, Y. Shi, J.C. Shillcock, M. Telefont, W. Van Geit, J. Villafranca Diaz, R. Walker, Y. Wang, S.M. Zaninetta, J. DeFelipe, S.L. Hill, J. Muller, I. Segev, F. Schürmann, E.B. Muller, and H. Markram, The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex. Front. Neural Circuits, 2015, 44.
842015M. Reimann, E.Muller, S.Ramaswamy, H.Markram: An Algorithm to Predict the Connectome of Neural Microcircuits. 2015. Frontiers in Neural Circuits 9 2015, 28.

832015A. Devresse, F. Delalondre, F. Schürmann, Blue Brain Project Fully Automated Workflows and Ecosystem to guarantee Scientific Result Reproducibility across Platforms, Software Environment and Systems. International Conference for High Performance Computing, Networking, Storage and Analysis 2015, Austin, Texas.
822015 T. Ewart, S. Yates, F. Cremonesi, P. Kumbhar, F. Schuermann, F. Delalondre, Performance Evaluation of the IBM POWER8 system to Support Computational Neuroscientific Application Using Morphologically Detailed Neurons. PMBS15 Workshop, Supercomputing 2015, Austin, Texas.
812015M.Abdellah, A.Bilgilli, S.Eilemann, H.Markram, F.Schürmann : Physically-based in silico light sheet microscopy for visualizing fluorescent brain models. BMC Bioinformatics. 2015 Aug 13;16 Suppl 11:S8
802015V.Delattre, D.Keller, M.Perich, H.Markram, E.B.Muller: Network-timing-dependent plasticity. Front Cell Neurosci. 2015 Ju 9;9:220
792015Anastassiou CA, Perin R, Buzsáki G, Markram H, Koch C. Cell type- and activity-dependent extracellular correlates of intracellular spiking. J Neurophysiol. 2015 Jul;114(1):608-23. doi: 10.1152/jn.00628.2014
782015S.Ramaswamy, H.Markram: Anatomy and Physiology of the thick-tufted layer 5 pyramidal neuron, Front Cell Neurosci. 2015; 9:233
772015D.Keller, N.Babai, O.Kochubey, Y.Han, H.Markram, F.Schürmann, R.Schneggenburger: An Exclusion Zone for Ca2+ Channels around Docked Vesicles Explains Release Control by Multiple Channels at a CNS Synapse, PLoS Comput Biol. 2015 May 7;11(5):e1004253.
762015Costantini I, Ghobril JP, Di Giovanna AP, Allegra Mascaro AL, Silvestri L, Müllenbroich MC, Onofri L, Conti V, Vanzi F, Sacconi L, Guerrini R, Markram H, Iannello G, Pavone FS A versatile clearing agent for multi-modal brain imaging. Scientific Reports. 2015 May 7;5:9808. doi: 10.1038/srep09808.
752015Frackowiak R, Markram H. The future of human cerebral cartography: a novel approach. Philos Trans R Soc Lond B Biol Sci. 2015 May 19;370(1668). pii: 20140171. doi: 10.1098/rstb.2014.0171.
742015Vannucci, 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., and 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 simulation. In 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 1179–1184. https://ieeexplore.ieee.org/document/7363512..
732015X.Vasques, R.Richardet, SL.Hill, D.Slater, J-C.Chappelier, E.Pralong. J.Bloh, B.Draganski, L.Cif: Automatic target validation based on neuroscientific literature mining for tractography, Front Neuroanat. 2015 May 27;9:66
722015R.Richardet, J-C.Chappelier, M.Telefont, S.Hill: Large-scale extraction of brain connectivity from the neuroscientific literature, Bioinformatics. 2015 May; 31(10):1640-1647
712015S. Ramaswamy and E. Muller, Cell-type specific modulation of neocortical UP and DOWN states. Frontiers in Cellular Neuroscience, 9:370, 2015
702015S. Ramaswamy, Exciting times for inhibition: GABAergic synaptic transmission in dentate gyrus interneuron networks. Frontiers in Neural Circuits, 9:13, 2015.
692015 E. Muller, J. A. Bednar Diesmann M., M.-O., Gewaltig, M. Hines , A.P. Davison,. Python in Neuroscience. Frontiers in Neuroinformatics, 2015, 9, doi: 10.3389/fninf.2015.00011
682015Wybo, W.A.M., Boccalini, D., Torben-Nielsen, B., and Gewaltig, M.-O. (2015). A Sparse Reformulation of the Green’s Function Formalism Allows Efficient Simulations of Morphological Neuron Models. Neural Comput 27, 2587–2622. 10.1162/NECO_a_00788
672015Tiesinga, P., Bakker, R., Hill, S., and Bjaalie, J.G. (2015). Feeding the human brain model. Curr. Opin. Neurobiol. 32, 107–114. doi:10.1016/j.conb.2015.02.003
662015Jolivet, R., Coggan, J.S., Allaman, I., and Magistretti, P.J. (2015). Multi-timescale modeling of activity-dependent metabolic coupling in the neuron-glia-vasculature ensemble. PLoS Comput. Biol. 11, e1004036. doi:10.1371/journal.pcbi.1004036
652015E.Hay and I.Segev: Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits. Cerebral Cortex, 2014 Sep 9
642014F.Tauheed, T.Heinis, F.Schürmann, H.Markram, A.Ailamaki : OCTOPUS: Efficient Query Execution on Dynamic Mesh Datasets, In Proceedings of the 30th IEEE International Conference on Data Engineering. Chicago, USA, March 2014
632014F.Schürmann, F.Delalondre, P.S.Kumbhar, J.Biddiscombe, M.Gila, D.Tacchella, A.Curioni, B.Metzler, P.Morjan, J.Fenkes, M.M.Franceschini, R.S.Germain, L.Schneidenbach, T.J.C.Ward, B.G.Fitch: Rebasing I/O for Scientific Computing: Leveraging Storage Class Memory in an IBM BlueGene/Q Supercomputer. In J.M. Kunkel, T. Ludwig, and H.W. Meuer (Eds.): ISC 2014, LNCS 8488, pp. 331--347. Springer International Publishing Switzerland (2014)
622014T.Ewart, F.Delalondre, F.Schürmann: Cyme: A Library Maximizing SIMD Computation on User-Defined Containers. In J.M. Kunkel, T. Ludwig, and H.W. Meuer (Eds.): ISC 2014, LNCS 8488, pp. 440--449. Springer International Publishing Switzerland (2014)
612014S.Muralidhar, Y.Wang, H.Markram: Synaptic and cellular organization of layer 1 of the developing rat somatosensory cortex. Front Neuroanat. 2014 Jan 16;7:52. doi: 10.3389/fnana.2013.00052. eCollection 2013.
602014MO.Gewaltig and R.Cannon: Current practice in software development for computational neuroscience and how to improve it. 2014. PLoS Comput Biol. 10(1)
592014J.DeFelipe, E.Garrido, H.Markram: The death of Cajal and the end of scientific romanticism and individualism. Trends Neurosci. 37(10):525-7 (2014)
582014Adaszewski, S. (2014). Mynodbcsv: lightweight zero-config database solution for handling very large C SV files. PLoS ONE 9, e103319. doi:10.1371/journal.pone.0103319
572014Babai, N., Kochubey, O., Keller, D., and Schneggenburger, R. (2014). An alien divalent ion reveals a major role for Ca2+ buffering in controlling slow transmitter release. J. Neurosci. 34, 12622–12635.
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562014Kriener, B., Enger, H., Tetzlaff, T., Plesser, H.E., Gewaltig, M.-O., and Einevoll, G.T. (2014). Dynamics of self-sustained asynchronous-irregular activity in random networks of spiking neurons with strong synapses. Front Comput Neurosci 8, 136. doi: 10.3389/fncom.2014.00136
552014Toledo-Rodriguez, M., and Markram, H. (2014). New Edition: Single-cell RT-PCR, a technique to decipher the electrical, anatomical, and genetic determinants of neuronal diversity. In: Martina M., Taverna S. (eds)Patch-Clamp Methods and Protocols. Methods in Molecular Biology (Methods and Protocols). In Methods in Molecular Biology, pp. 143–158. [For accessible earlier version see Toledo-Rodriquez et al 2007.]
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542013M.W. Reimann, C.A.Anastassiou, R.Perin, S.L.Hill, H. Markram, C. Koch: A biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents. Neuron, 79(2), 375-390, 2013. doi: 10.1016/j.neuron.2013.05.023
532013E. Hay, F. Schürmann, H. Markram, I. Segev: Preserving axosomatic spiking features despite diverse dendritic morphology. J Neurophysiol, 109(12), 2972-2981, 2013. doi: 10.1152/jn.00048.2013
522013J.B. Hernando, J. Biddiscombe, B. Bohara, S. Eilemann, F. Schürmann: Practical parallel rendering of detailed neuron simulations, EGPGV 2013
512013R.Perin, M.Telefont, H.Markram: Computing the size and number of neuronal clusters in local circuits, Front Neuroanat. 2013;7:1. doi: 10.3389/fnana.2013.00001. Epub 2013 Feb 19.
502013A.Loebel, JV.LeBe, MJ.Richardson, H.Markram, A.Herz: Matched pre- and post-synaptic changes underlie synaptic plasticity over long time scales. 2013. J Neurosci. 33(15):6257-66.
492013H.Markram: Seven challenges for Neuroscience. 2013. Functional Neurology. 28(3):145-51
482013ER.Kandel, H.Markram, PM.Matthews, R.Yuste, C.Koch: Neuroscience thinks big (and collaboratively). 2013. Nat Rev Neurosci. 14(9):659-64.
472013J.DeFelipe et al. [42 authors]: New insights into the classification and nomenclature of cortical GABAergic interneurons. 2013. Nat Rev Neurosci. 14(3):202-16.
462013Wybo, W.A.M., Stiefel, K.M., and Torben-Nielsen, B. (2013). The Green’s function formalism as a bridge between single- and multi-compartmental modeling. Biol Cybern 107, 685–694.
doi:10.1007/s00422-013-0568-0
452013S.Druckmann, S.Hill, F.Schürmann, H.Markram, I.Segev : A Hierarchical Structure of Cortical Interneuron Electrical Diversity Revealed by Automated Statistical Analysis, Cerebral Cortex, (2012), doi: 10.1093/cercor/bhs290
442012Markram, H., Gerstner, W., and Sjöström, P.J. (2012). Editorial Article: Spike-timing-dependent plasticity: a comprehensive overview. Front Synaptic Neurosci 4, 2.
doi: 10.3389/fnsyn.2012.00002
432012Tauheed, F., Biveinis, L., Heinis, T., Schurmann, F., Markram, H., and Ailamaki, A. (2012a). Accelerating Range Queries for Brain Simulations. In Proceedings of the 2012 IEEE 28th International Conference on Data Engineering, (Washington, DC, USA: IEEE Computer Society), pp. 941–952.
doi: 10.1109/ICDE.2012.56
422012Hernando, J., Schürmann, F., and Pastor, L. (2012). Towards real-time visualization of detailed neural tissue models: View frustum culling for parallel rendering. In IEEE Symposium on Biological Data Visualization (BioVis), (IEEE), pp. 25–32.
doi 10.1109/BioVis.2012.6378589
412012S.L.Hill, Y.Wang, I.Riachi, F.Schürmann, H.Markram: Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits, PNAS, Published online before print September 18, 2012, doi: 10.1073/pnas.1202128109
402012A.Gidon and I.Segev: Principles governing the operation of synaptic inhibition in dendrites, Neuron, 2012 Jul 26;75(2):330-41
392012 F.Tauheed, T.Heinis, F.Schürmann, H.Markram, A.Ailamaki: SCOUT: Prefetching of Latent Structure Following Queries, VLDB 2012
382012G.Khazen, S.L.Hill, F.Schürmann , and H.Markram: Combinatorial Expression Rules of Ion Channel Genes in Juvenile Rat (Rattus norvegicus) Neocortical Neurons, PLoS One, 7(4): e34786. doi:10.1371/journal.pone.0034786
372012S.Eilemann, A.Bilgili, M.Abdellah, J.Hernando, M.Makhinya, R.Pajarola, and F.Schürmann: Parallel Rendering on Hybrid Multi-GPU Clusters, EGPGV 2012
362012S.Lasserre, J.Hernando, S.Hill, F.Schürmann, P. de Miguel Anasagasti, G.Abou Jaoudé, H.Markram : A Neuron Mesh Representation for Visualization of Electrophysiological Simulations, IEEE Transactions on Visualization and Computer Graphics, 18 (2): p. 214-217.
352012S.Ramaswamy, S.L.Hill, J.G.King, F.Schürmann, Y.Wang, and H.Markram: Intrinsic Morphological Diversity of Thick-tufted Layer 5 Pyramidal Neurons Ensures Robust and Invariant Properties of in silico Synaptic Connections. J Physiol. 2012 Feb 15;590(Pt 4):737-52. Epub 2011 Nov 14.
342011R.Ranjan, G.Khazen, L.Gambazzi, S.Ramaswamy, S.L.Hill, F.Schürmann, and H.Markram: Channelpedia: an integrative and interactive database for ion channels, Front. Neuroinform 2011. 5:36. doi: 10.3389/fninf.2011.00036
332011M.Hines, S.Kumar, and F.Schürmann: Comparison of neuronal spike exchange methods on a Blue Gene/P supercomputer. Front. Comput. Neurosci 2011. 5:49. doi: 10.3389/fncom.2011.00049
322011E.Hay, S.L.Hill, F.Schürmann, H.Markram, and I.Segev: Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties. PLoS Computational Biology 2011, 7(7): e1002107. doi:10.1371/journal.pcbi.1002107
312011S.Druckmann, T.K.Berger, F.Schürmann, S.L.Hill, H.Markram, and I.Segev: Effective stimuli for constructing reliable neuron models,
Plos Computational Biology, 2011, 7(8): e1002133. doi:10.1371/journal.pcbi.1002133
302011R.Perin, T.K.Berger, and H.Markram: A synaptic organizing principle for cortical neuronal groups, PNAS, 2011, 108 (12)
292011S.Romand, Y.Wang, M.Toledo-Rodriguez, and H.Markram: Morphological development of thick-tufted layer v pyramidal cells in the rat somatosensory cortex, Front Neuroanat. 2011 5:5, doi: 10.3389/fnana.2011.00005
282011CA.Anastassiou, R.Perin, H.Markram, and C.Koch: Ephaptic coupling of cortical neurons, Nat Neurosci. 2011 Feb;14(2):217-23
272011H. Markram, W. Gerstner, PJ. Sjöström: A history of spike-timing-dependent plasticity. Front Synaptic Neurosci. 2011;3:4. Epub 2011 Aug 29.
262011H. Markram, R. Perin: Innate neural assemblies for lego memory. Front Neural Circuits. 2011;5:6. Epub 2011 May 16.
252010TK.Berger, G.Silberberg, R.Perin, and H.Markram: Brief bursts self-inhibit and correlate the pyramidal network, PLoS Biol. 2010 Sep 7;8(9)
242010L.Bar-Ilan, A.Gidon, and I.Segev: Inter-regional synaptic competition in neurons with multiple STDP-inducing signals, J Neurophysiol (December 1, 2010), doi:10.1152/jn.00612.2010.
232009A.Loebel, G.Silberberg, D.Helbig, H.Markram, M.Tsodyks, MJ.Richardson: Multiquantal release underlies the distribution of synaptic efficacies in the neocortex, Front Comput Neurosci. 2009; 3:27
222009TK.Berger, R.Perin, G.Silberberg, and H.Markram: Frequency-dependent disynaptic inhibition in the pyramidal network: a ubiquitous pathway in the developing rat neocortex, J Physiol. 2009 Nov 15;587(Pt 22):5411-25
212009J.G.King, M.Hines, S.Hill, P.H.Goodman, H.Markram, F.Schürmann: A component-based extension framework for large-scale parallel simulations in NEURON , Front Neuroinformatics, 3:10, doi:10.3389/neuro.11.010.2009.
202009Anwar H., Riachi I., Schürmann F., Markram H. (2009). “An approach to capturing neuron morphological diversity,” in Computational Neuroscience: Realistic Modeling for Experimentalistsed. De Schutter E., editor. (Cambridge: The MIT Press) 211–232. https://mitpress.mit.edu/books/computational-modeling-methods-neuroscientists
192008J.Kozloski, K.Sfyrakis, S.Hill, F.Schürmann, C.Peck, H.Markram: Identifying, tabulating, and analyzing contacts between branched neuron morphologies, IBM Journal of Research and Development, Vol 52, Number 1/2, 2008
182008M.Hines, H.Eichner, F.Schürmann: Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors, J. Comput. Neurosci., 25(1):203-10, 2008
172008M.Hines, H.Markram, F.Schürmann: Fully Implicit Parallel Simulation of Single Neurons, J. Comput. Neurosci., 25(3):439-48, 2008
162008S.Druckmann, T.Berger, S.Hill, F.Schürmann, H.Markram, I.Segev: Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data, Biol Cybern, 99(4-5):371-9, 2008
152008C.Calì, TK.Berger, M.Pignatelli, A.Carleton, H.Markram, M.Giugliano: Inferring connection proximity in networks of electrically coupled cells by subthreshold frequency response analysis, J Comput Neurosci. 2008 Jun;24(3):330-45. Epub 2007 Nov 28.
142008O.Melamed, O.Barak, G.Silberberg, H.Markram, M.Tsodyks: Slow oscillations in neural networks with facilitating synapses, J Comput Neurosci. 2008 Oct;25(2):308-16.
132008GA.Ascoli, Alonso-Nanclares L, Anderson SA, Barrionuevo G, Benavides-Piccione R, Burkhalter A, Buzsáki G, Cauli B, Defelipe J, Fairén A, Feldmeyer D, Fishell G, Fregnac Y, Freund TF, Gardner D, Gardner EP, Goldberg JH, Helmstaedter M, Hestrin S, Karube F, Kisvárday ZF, Lambolez B, Lewis DA, Marin O, Markram H, Muñoz A, Packer A, Petersen CC, Rockland KS, Rossier J, Rudy B, Somogyi P, Staiger JF, Tamas G, Thomson AM, Toledo-Rodriguez M, Wang Y, West DC, Yuste R.: Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex, Nat Rev Neurosci. 2008 Jul;9(7):557-68
122008H.Markram: Fixing the location and dimensions of functional neocortical columns, HFSP J. 2008 Jun;2(3):132-5.
112007G.Silberberg and H.Markram: Disynaptic inhibition between neocortical pyramidal cells mediated by Martinotti cells, Neuron. 2007 Mar 1;53(5):735-46.
102007H.Markram: Bioinformatics: industrializing neuroscience. Nature. 2007 Jan 11;445(7124):160-1.
92007A.Abid, A.Jan, L.Francioli, K.Sfyrakis, and F.Schürmann: Keyword Based Indexing and Searching over Storage Resource Broker. OTM Conferences, 2007, Proceedings, Part II. Lecture Notes in Computer Science 4804 Springer 2007, ISBN 978-3-540-76835-7, pp. 1233-43
82007S.Druckmann, Y.Banitt, A.Gidon, F.Schürmann, H.Markram, and I.Segev: A Novel Multiple Objective Optimization Framework for Constraining Conductance-Based Neuron Models by Experimental Data, Frontiers in Neuroscience, Vol. 1, Issue 1, 2007
72007M.Toledo-Rodriguez and H.Markram: Single-cell RT-PCR, a technique to decipher the electrical, anatomical, and genetic determinants of neuronal diversity, Methods Mol Biol. 2007;403:123-39.
22007JV.Le Bé, G.Silberberg, Y.Wang, and H.Markram: Morphological, electrophysiological, and synaptic properties of corticocallosal pyramidal cells in the neonatal rat neocortex, Cereb Cortex. 2007 Sep;17(9):2204-13.
62006M.Migliore, C.Cannia, W.W.Lytton, H.Markram, and M.L.Hines: Parallel network simulations with NEURON, J Comput Neurosci. 2006 Oct;21(2):119-29.
52006H.Markram: The blue brain project. Nat Rev Neurosci. 7, 153-160, 2006
42006Y.Wang, H.Markram, PH.Goodman, TK.Berger , J.Ma PS.Goldman-Rakic: Heterogeneity in the pyramidal network of the medial prefrontal cortex, Nat Neurosci. 2006 Apr;9(4):534-42.
32006JV.Le Bé and H.Markram: Spontaneous and evoked synaptic rewiring in the neonatal neocortex, PNAS. 2006 Aug 29;103(35):13214-9.
12005 A.J. Muhammad, H. Markram, NEOBASE: Databasing the Neocortical Microcircuit, Stud Health Technol Inform. 2005;112:167-77.