Publications

PublicationYearAuthors
2392023

Keller, D., Verasztó, C., & Markram, H. (2023). Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization. Frontiers in Neuroanatomy, 17. https://doi.org/10.3389/fnana.2023.1118170
2382023

Iavarone, E., Simko, J., Shi, Y., Bertschy, M., García-Amado, M., Litvak, P., Kaufmann, A.-K., O’Reilly, C., Amsalem, O., Abdellah, M., Chevtchenko, G., Coste, B., Courcol, J.-D., Ecker, A., Favreau, C., Fleury, A. C., Van Geit, W., Gevaert, M., Guerrero, N. R, Herttuainen, J., Ivaska, G., Kerrien, S., King, J.G., Kumbhar, P., Lurie, P., Magkanaris, I., Muddapu, V.R., Nair, J., Pereira, F.L., Perin, R., Petitjean, F., Ranjan, R., Reimann, M., Soltuzu, L., Sy, M.F., Tuncel, M.A., Ulbrich, A., Wolf, M., Clascá, F., Markram, H., & Hill, S. L. (2023). Thalamic control of sensory processing and spindles in a biophysical somatosensory thalamoreticular circuit model of wakefulness and sleep. Cell Reports, 42(3), 112200. https://doi.org/10.1016/j.celrep.2023.112200
2372023

Ecker, A., Santander, D. E., Bolaños-Puchet, S., Isbister, J. B., & Reimann, M. W. (2023). Cortical cell assemblies and their underlying connectivity: An in silico study. bioRxiv, 2023.02.24. https://doi.org/10.1101/2023.02.24.529863
2362023

Roussel, Y., Verasztó, C., Rodarie, D., Damart, T., Reimann, M., Ramaswamy, S., Markram, H., & Keller, D. (2023). Mapping of morpho-electric features to molecular identity of cortical inhibitory neurons. PLOS Computational Biology, 19(1), e1010058. https://doi.org/10.1371/journal.pcbi.1010058
2352022

Rodarie, D., Verasztó, C., Roussel, Y., Reimann, M., Keller, D., Ramaswamy, S., Markram, H., & Gewaltig, M.-O. (2022). A method to estimate the cellular composition of the mouse brain from heterogeneous datasets. PLOS Computational Biology, 18(12), e1010739. https://doi.org/10.1371/journal.pcbi.1010739
2342022

Rosenberg, N., Reva, M., Binda, F., Restivo, L., Depierre, P., Puyal, J., Briquet, M., Bernardinelli, Y., Rocher, A.-B., Markram, H., & Chatton, J.-Y. (2022). Overexpression of UCP4 in astrocytic mitochondria prevents multilevel dysfunctions in a mouse model of Alzheimer’s disease. Glia, 1-17. https://doi.org/10.1002/glia.24317
2332022

Shillcock, J. C., Thomas, D., Ipsen, J. H., & Brown, A. D. (2022). Macromolecular crowding is surprisingly unable to deform the structure of a model biomolecular condensate. bioRxiv, 14 December 2022. https://doi.org/10.1101/2022.12.12.520052
2322022

Reva, M., Rössert, C., Arnaudon, A., Damart, T., Mandge, D., Tuncel, A., Ramaswamy, S., Markram, H., & Van Geit, W. (2022). A universal workflow for creation, validation and generalization of detailed neuronal models. bioRxiv, 13 December 2022. https://doi.org/10.1101/2022.12.13.520234
2312022

Abdellah, M., Cantero, J. J. G., Guerrero, N. R., Foni, A., Coggan, J. S., Calì, C., Agus, M., Zisis, E., Keller, D., Hadwiger, M., Magistretti, P. J., Markram, H., & Schürmann, F. (2022). Ultraliser: A framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience. Briefings in Bioinformatics, bbac491. https://doi.org/10.1093/bib/bbac491
2302022

Colangelo, C., Muñoz, A., Antonietti, A., Antón-Fernández, A., Romani, A., Herttuainen, J., Markram, H., DeFelipe, J., & Ramaswamy, S. (2022). Neuromodulatory organization in the developing rat somatosensory cortex.bioRxiv, 13 November 2022. https://doi.org/10.1101/2022.11.11.516108
2292022

Saxena, D., Arnaudon, A., Cipolato, O., Gaio, M., Quentel, A., Yaliraki, S., Pisignano, D., Camposeo, A., Barahona, M., & Sapienza, R. (2022). Sensitivity and spectral control of network lasers. Nature Communications, 13(1), 6493. https://doi.org/10.1038/s41467-022-34073-3
2282022

Chen, W., Carel, T., Awile, O., Cantarutti, N., Castiglioni, G., Cattabiani, A., Del Marmol, B., Hepburn, I., King, J. G., Kotsalos, C., Kumbhar, P., Lallouette, J., Melchior, S., Schürmann, F., & De Schutter, E. (2022). STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale. Frontiers in Neuroinformatics, 16, 883742. https://doi.org/10.3389/fninf.2022.883742
2272022

Colombo, G., Cubero, R. J. A., Kanari, L., Venturino, A., Schulz, R., Scolamiero, M., Agerberg, J., Mathys, H., Tsai, L.-H., Chachólski, W., Hess, K., & Siegert, S. (2022). A tool for mapping microglial morphology, morphOMICs, reveals brain-region and sex-dependent phenotypes. Nature Neuroscience, 25(10), 1379–1393. https://doi.org/10.1038/s41593-022-01167-6
2262022

Denizdurduran, B., Markram, H., & Gewaltig, M.-O. (2022). Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning. Biological Cybernetics. https://doi.org/10.1007/s00422-022-00940-x
2252022

Abdellah, M., Garcia Cantero, J. J., Foni, A., Román Guerrero, N., Boci, E., & Schürmann, F. (2022). Meshing of spiny neuronal morphologies using union operators. In P. Vangorp & M. J. Turner (Eds.), Computer Graphics and Visual Computing (CGVC) conference proceedings (Graphics section). The Eurographics Association, UK. https://doi.org/10.2312/cgvc.20221168
2242022

Bologna, L. L., Smiriglia, R., Lupascu, C. A., Appukuttan, S., Davison, A. P., Ivaska, G., Courcol, J.-D., & Migliore, M. (2022). The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models. Frontiers in Neuroinformatics, 16, 991609. https://doi.org/10.3389/fninf.2022.991609
2232022

Guyonnet-Hencke, T., & Reimann, M. W. (2022). A parcellation scheme of mouse isocortex based on reversals in connectivity gradients. bioRxiv. 31 August 2022. https://doi.org/10.1101/2022.08.30.505842
2222022

Sy, M. F., Roman, B., Kerrien, S., Mendez, D. M., Genet, H., Wajerowicz, W., Dupont, M., Lavriushev, I., Machon, J., Pirman, K., Neela Mana, D., Stafeeva, N., Kaufmann, A.-K., Lu, H., Lurie, J., Fonta, P.-A., Martinez, A. G. R., Ulbrich, A. D., Lindqvist, C., Jimenez, S., Rotenberg, D., Markram, H., Hill, S. L. (2022). Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science. Semantic Web, 1–31. https://doi.org/10.3233/SW-222974
2212022

Shillcock, J. C., Lagisquet, C., Alexandre, J., Vuillon, L., & Ipsen, J. H. (2022). Model biomolecular condensates have heterogeneous structure quantitatively dependent on the interaction profile of their constituent macromolecules. Soft Matter (Royal Society of Chemistry). https://doi.org/10.1039/D2SM00387B
2202022

Appukuttan, S., Bologna, L. L., Schürmann, F., Migliore, M., & Davison, A. P. (2022). EBRAINS Live Papers—Interactive Resource Sheets for Computational Studies in Neuroscience. Neuroinformatics. https://doi.org/10.1007/s12021-022-09598-z.
2192022

Arnaudon, A., Peach, R. L., Petri, G., & Expert, P. (2022). Connecting Hodge and Sakaguchi-Kuramoto through a mathematical framework for coupled oscillators on simplicial complexes. Communications Physics, 5(1), 211. https://doi.org/10.1038/s42005-022-00963-7
2182022

Reimann, M. W., Bolaños-Puchet, S., Courcol, J.-D., Santander, D. E., Arnaudon, A., Coste, B., Delemontex, T., Devresse, A., Dictus, H., Dietz, A., Ecker, A., Favreau, C., Ficarelli, G., Gevaert, M., Hernando, J. B., Herttuainen, J., Isbister, J. B., Kanari, L., Keller, D., King, J., Kumbhar, P., Lapere, S., Lazovskis, J., Lu, H., Ninin, N., Pereira, F., Planas, J., Pokorny, C., Riquelme, J.L., Romani, A., Shi, Y., Smith, J.P., Sood, V., Srivastava, M., Van Geit, W., Vanherpe, L., Wolf, M., Levi, R., Hess, K., Schürmann, F., Muller, E.B., Ramaswamy, S., Markram, H. (2022). Modeling and simulation of rat non-barrel somatosensory cortex. Part I: Modeling anatomy. bioRxiv. 15 August 2022. http://biorxiv.org/lookup/doi/10.1101/2022.08.11.503144
2172022

Nandi, A., Chartrand, T., Van Geit, W., Buchin, A., Yao, Z., Lee, S. Y., Wei, Y., Kalmbach, B., Lee, B., Lein, E., Berg, J., Sümbül, U., Koch, C., Tasic, B., & Anastassiou, C. A. (2022).Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types. Cell Reports, 40(6), 111176. https://doi.org/10.1016/j.celrep.2022.111176
2162022

Buccino, A. P., Damart, T., Bartram, J., Mandge, D., Xue, X., Zbili, M., Gänswein, T., Jaquier, A., Emmenegger, V., Markram, H., Hierlemann, A., & Van Geit, W. (2022). A multi-modal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays. bioRxiv. 5 August 2022. https://doi.org/10.1101/2022.08.03.502468
2152022

Eriksson, O., Bhalla, U. S., Blackwell, K. T., Crook, S. M., Keller, D., Kramer, A., Linne, M.-L., Saudargienė, A., Wade, R. C., & Hellgren Kotaleski, J. (2022). Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows. eLife, 11, e69013. https://doi.org/10.7554/eLife.69013
2142022

Hunt, S., Leibner, Y., Mertens, E. J., Barros-Zulaica, N., Kanari, L., Heistek, T. S., Karnani, M. M., Aardse, R., Wilbers, R., Heyer, D. B., Goriounova, N. A., Verhoog, M. B., Testa-Silva, G., Obermayer, J., Versluis, T., Benavides-Piccione, R., de Witt-Hamer, P., Idema, S., Noske, D. P., D. P., Baayen, J. C., Lein, E. S., DeFelipe, J., Markram, H., Mansvelder, H. D., Schürmann, F., Segev, I., & de Kock, C. P. J. (2022). Strong and reliable synaptic communication between pyramidal neurons in adult human cerebral cortex. Cerebral Cortex, bhac246. https://doi.org/10.1093/cercor/bhac246
2132022

Awile, O., Kumbhar, P., Cornu, N., Dura-Bernal, S., King, J. G., Lupton, O., Magkanaris, I., McDougal, R. A., Newton, A. J. H., Pereira, F., Săvulescu, A., Carnevale, N. T., Lytton, W. W., Hines, M. L., & Schürmann, F. (2022). Modernizing the NEURON simulator for sustainability, portability, and performance. Research topic: Neuroscience, computing, performance, and benchmarks: Why it matters to neuroscience how fast we can compute). Frontiers in Neuroinformatics, 16. https://doi.org/10.3389/fninf.2022.884046
2122022

Tourbier, S., Rue-Queralt, J., Glomb, K., Aleman-Gomez, Y., Mullier, E., Griffa, A., Schöttner, M., Wirsich, J., Tuncel, M. A., Jancovic, J., Cuadra, M. B., & Hagmann, P. (2022). Connectome Mapper 3: A flexible and open-source pipeline software for multiscale multimodal human connectome mapping. Journal of Open Source Software, 7(74), 4248. https://doi.org/10.21105/joss.04248
2112022

Peach, R., Arnaudon, A., & Barahona, M. (2022). Relative, local and global dimension in complex networks. Nature Communications, 13(1), 3088. https://doi.org/10.1038/s41467-022-30705-w
2102022

Chindemi, G., Abdellah, M., Amsalem, O., Benavides-Piccione, R., Delattre, V., Doron, M., Ecker, A., Jaquier, A. T., King, J., Kumbhar, P., Monney, C., Perin, R., Rössert, C., Tuncel, A. M., Van Geit, W., DeFelipe, J., Graupner, M., Segev, I., Markram, H., & Muller, E. B. (2022). A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex. Nature Communications, 13(1), 3038. https://doi.org/10.1038/s41467-022-30214-w
2092022

Schürmann, F., Courcol, J.-D., & Ramaswamy, S. (2022). Computational concepts for reconstructing and simulating brain tissue. Chapter 10. In: Giugliano, Negrello, Linaro (eds.) Computational Modelling of the Brain: Modelling Approaches to Cells, Circuits and Networks. Series: Advances in Experimental Medicine and Biology (vol. 1359, pp. 237–259). Springer International Publishing. https://doi.org/10.1007/978-3-030-89439-9_10
2082022

Romani, A., Schürmann, F., Markram, H., & Migliore, M. (2022). Reconstruction of the hippocampus. Chapter 11. In: Giugliano, Negrello, Linaro (eds.) Computational Modelling of the Brain: Modelling Approaches to Cells, Circuits and Networks. Series: Advances in Experimental Medicine and Biology (vol. 1359, pp. 261–283). Springer International Publishing. https://doi.org/10.1007/978-3-030-89439-9_11
2072022

Honaryar, H., LaNasa, J. A., Hickey, R. J., Shillcock, J. C., & Niroobakhsh, Z. (2022). Investigating the morphological transitions in an associative surfactant ternary system. Soft Matter, 18(13), 2611–2633. https://doi.org/10.1039/D1SM01668G
2062022

Shillcock, J. C., Hastings, J., Riguet, N., & Lashuel, H. A. (2022). Non-monotonic fibril surface occlusion by GFP tags from coarse-grained molecular simulations. Computational and Structural Biotechnology Journal, 20, 309–321. https://doi.org/10.1016/j.csbj.2021.12.017
2052022

Coggan, J. S., Keller, D., Markram, H., Schürmann, F., & Magistretti, P. J. (2022). Representing stimulus information in an energy metabolism pathway. Journal of Theoretical Biology, 540, 111090. https://doi.org/10.1016/j.jtbi.2022.111090
2042022

Gillespie, T. H., Tripathy, S. J., Sy, M. F., Martone, M. E., & Hill, S. L. (2022). The Neuron Phenotype Ontology: A FAIR approach to proposing and classifying neuronal types. Neuroinformatics. https://doi.org/10.1007/s12021-022-09566-7
2032022

Kanari, L., Dictus, H., Chalimourda, A., Arnaudon, A., Van Geit, W., Coste, B., Shillcock, J., Hess, K., & Markram, H. (2022). Computational synthesis of cortical dendritic morphologies. Cell Reports, 39(1), 110586. https://doi.org/10.1016/j.celrep.2022.110586
2022022

Shapira, G., Marcus-Kalish, M., Amsalem, O., Van Geit, W., Segev, I., & Steinberg, D. M. (2022). Statistical emulation of neural simulators: Application to neocortical L2/3 large basket cells. Frontiers in Big Data, 5. https://doi.org/10.3389/fdata.2022.789962
2012022

Reimann, M. W., Riihimäki, H., Smith, J. P., Lazovskis, J., Pokorny, C., & Levi, R. (2022). Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies. PLOS ONE, 17(1), e0261702. https://doi.org/10.1371/journal.pone.0261702
2002022

Shillcock, J. C., Thomas, D. B., Beaumont, J. R., Bragg, G. M., Vousden, M. L., & Brown, A. D. (2021).Coupling bulk phase separation of disordered proteins to membrane domain formation in molecular simulations on a bespoke compute fabric. Membranes, 12(1), 17. https://doi.org/10.3390/membranes12010017
1992021

Tata Ramalingasetty, S., Danner, S. M., Arreguit, J., Markin, S. N., Rodarie, D., Kathe, C., Courtine, G., Rybak, I. A., & Ijspeert, A. J. (2021). A whole-body musculoskeletalmodel of the mouse. IEEE Access, 9, 163861–163881. https://doi.org/10.1109/ACCESS.2021.3133078.
1982021

Santos, J. P. G., Pajo, K., Trpevski, D., Stepaniuk, A., Eriksson, O., Nair, A. G., Keller, D., Hellgren Kotaleski, J., & Kramer, A. (2021). A modular workflow for model building, analysis, and parameter estimation in systems biology and neuroscience. Neuroinformatics. Online: 28 October 2021. https://doi.org/10.1007/s12021-021-09546-3
1972021

Shichkova, P., Coggan, J. S., Markram, H., & Keller, D. (2021). A standardized brain molecular atlas: A resource for systems modeling and simulation. Frontiers in Molecular Neuroscience, 14, 251. https://doi.org/10.3389/fnmol.2021.604559
1962021

Simko, J., & Markram, H. (2021). Morphology, physiology and synaptic connectivity of local interneurons in the mouse somatosensory thalamus. The Journal of Physiology, 599(22), 5085–5101. https://doi.org/10.1113/JP281711
1952021

Gal, E., Amsalem, O., Schindel, A., London, M., Schürmann, F., Markram, H., & Segev, I. (2021). The role of hub neurons in modulating cortical dynamics. Frontiers in Neural Circuits, 15, 96. https://doi.org/10.3389/fncir.2021.718270
1942021

Zisis, E., Keller, D., Kanari, L., Arnaudon, A., Gevaert, M., Delemontex, T., Coste, B., Foni, A., Abdellah, M., Calì, C., Hess, K., Magistretti, P. J., Schürmann, F., & Markram, H. (2021). Digital reconstruction of the neuro-glia-vascular architecture. Cerebral Cortex, 31(12), 5686–5703. https://doi.org/10.1093/cercor/bhab254
1932021

Curry, J., DeSha, J., Garin, A., Hess, K., Kanari, L., & Mallery, B. (2021). From trees to barcodes and back again II: Combinatorial and probabilistic aspects of a topological inverse problem. arXiv. 26 July 2021. https://doi.org/10.48550/arXiv.2107.11212https://doi.org/10.48550/arXiv.2107.11212
1922021

Pezeshkian, W., Shillcock, J. C., & Ipsen, J. H. (2021). Computational approaches to explore bacterial toxin entry into the host cell. Toxins, 13(7), 449. https://doi.org/10.3390/toxins13070449.
1912021Gosztolai, A., & Arnaudon, A. (2021). Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature. Nature Communications, 12(1), 4561. https://doi.org/10.1038/s41467-021-24884-1.
1902021Isbister, J. B., Reyes-Puerta, V., Sun, J.-J., Horenko, I., & Luhmann, H. J. (2021). Clustering and control for adaptation uncovers time-warped spike time patterns in cortical networks in vivo. Scientific Reports, 11(1), 15066. https://doi.org/10.1038/s41598-021-94002-0
1892021Logette, E., Lorin, C., Favreau, C., Oshurko, E., Coggan, J. S., Casalegno, F., Sy, M. F., Monney, C., Bertschy, M., Delattre, E., Fonta, P.-A., Krepl, J., Schmidt, S., Keller, D., Kerrien, S., Scantamburlo, E., Kaufmann, A.-K., & Markram, H. (2021). A machine-generated view of the role of blood glucose levels in the severity of COVID-19. Frontiers in Public Health, 9, 1068. https://doi.org/10.3389/fpubh.2021.695139
1882021Krepl, J., Casalegno, F., Delattre, E., Erö, C., Lu, H., Keller, D., Rodarie, D., Markram, H., & Schürmann, F. (2021). Supervised learning with perceptual similarity for multimodal gene expression registration of a mouse brain atlas. Frontiers in Neuroinformatics, 15, 37. https://doi.org/10.3389/fninf.2021.691918
1872021

Abdellah, M., Foni, A., Zisis, E., Guerrero, N. R., Lapere, S., Coggan, J. S., Keller, D., Markram, H., & Schürmann, F. (2021). Metaball skinning of synthetic astroglial morphologies into realistic mesh models for visual analytics and in silico simulations. Bioinformatics, 37(Supplement_1), i426–i433. https://doi.org/10.1093/bioinformatics/btab280

1862021

Peach, R. L., Arnaudon, A., Schmidt, J. A., Palasciano, H. A., Bernier, N. R., Jelfs, K. E., Yaliraki, S. N., & Barahona, M. (2021). HCGA: Highly comparative graph analysis for network phenotyping. Patterns, 2(4), 100227, Cell Press. https://doi.org/10.1016/j.patter.2021.100227
1852021

Newton, T. H., Reimann, M. W., Abdellah, M., Chevtchenko, G., Muller, E. B., & Markram, H. (2021). In silico voltage-sensitive dye imaging reveals the emergent dynamics of cortical populations.Nature Communications, 12(1), 3630. https://doi.org/10.1038/s41467-021-23901-7
1842021O’Reilly, C., Iavarone, E., Yi, J., & Hill, S. L. (2021). Rodent somatosensory thalamocortical circuitry: Neurons, synapses, and connectivity. Neuroscience & Biobehavioral Reviews, 126, 213–235. https://doi.org/10.1016/j.neubiorev.2021.03.015
1832021

Petersen, C. C. H., Knott, G. W., Holtmaat, A., & Schürmann, F. (2021). Toward biophysical mechanisms of neocortical computation after 50 years of barrel cortex research. Function, 2(1) zqaa046. Oxford Univ. Press for the American Physiological Society. https://doi.org/10.1093/function/zqaa046
1822021

Courcol, J.-D., Invernizzi, C. F., Landry, Z. C., Minisini, M., Baumgartner, D. A., Bonhoeffer, S., Chabriw, B., Clerc, E. E., Daniels, M., Getta, P., Girod, M., Kazala, K., Markram, H., Pasqualini, A., Martínez-Pérez, C., Peaudecerf, F. J., Peaudecerf, M. S., Pfreundt, U., Roller, B. R. K., Słomka, J., Vasse, M., Wheeler, J.D., Metzger, C.M.J.A., Stocker, R., and Schürmann, F. (2021). ARC: An open web-platform for request/supply matching for a prioritized and controlled COVID-19 response. Frontiers in Public Health, 9, 71. https://doi.org/10.3389/fpubh.2021.607677.
1812021

Sáray, S., Rössert, C. A., Appukuttan, S., Migliore, R., Vitale, P., Lupascu, C. A., Bologna, L. L., Van Geit, W., Romani, A., Davison, A. P., Muller, E., Freund, T. F., & Káli, S. (2021). HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data. PLOS Computational Biology, 17(1), e1008114. https://doi.org/10.1371/journal.pcbi.1008114
1802021

Schmuker, M., Kupper, R., Aertsen, A., Wachtler, T., & Gewaltig, M.-O. (2021). Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code. Biological Cybernetics, 115(2), 161–176. https://doi.org/10.1007/s00422-021-00866-w
1792020Magalhaes, B., & Schürmann, F. (2020). Efficient distributed transposition of large-scale multigraphs and high-cardinality sparse matrices.  arXiv, 10December2020. http://arxiv.org/abs/2012.0601 http://arxiv.org/abs/2012.06012.2.
1782020Kanari, L., Garin, A., & Hess, K. (2020). From trees to barcodes and back again: Theoretical and statistical perspectives. Algorithms, 2020, 13(12), 335 (Special issue: Topological Data Analysis). https://doi.org/10.3390/a13120335.
1772020Ecker, A., Romani, A., Sáray, S., Káli, S., Migliore, M., Falck, J., Lange, S., Mercer, A., Thomson, A. M., Muller, E., Reimann, M. W., & Ramaswamy, S. (2020). Data‐driven integration of hippocampal CA1 synaptic physiology in silico. Hippocampus, Wiley. 30(11), 1129–1145. https://doi.org/10.1002/hipo.23220.
1762020Ewart, T., Cremonesi, F., Schürmann, F., & Delalondre, F. (2020). Polynomial evaluation on superscalar architecture, applied to the elementary function ex. ACM Transactions on Mathematical Software, 46(3). Association for Computing Machinery. https://doi.org/10.1145/3408893.
1752020Abdellah, M., Guerrero, N. R., Lapere, S., Coggan, J. S., Keller, D., Coste, B., Dagar, S., Courcol, J.-D., Markram, H., & Schürmann, F. (2020). Interactive visualization and analysis of morphological skeletons of brain vasculature networks with VessMorphoVis. Bioinformatics, Oxford University Press. Vol. 36 (Supplement_1), i534–i541. https://doi.org/10.1093/bioinformatics/btaa461.
1742020Damart, T., Van Geit, W., & Markram, H. (2020). Data driven building of realistic neuron model using IBEA and CMA evolution strategies. GECCO ’20 Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 35–36. https://doi.org/10.1145/3377929.3398161
1732020Magalhães, B., Hines, M. L., Sterling, T., & Schürmann, F. (2020). Fully-asynchronous fully-implicit variable-order variable-timestep simulation of neural networks. In Krzhizhanovskaya, V. et al. (Eds.),ICCS 2020 Amsterdam: Lecture Notes in Computer Science, vol 12141. Springer, Cham. https://link.springer.com/chapter/10.1007%2F978-3-030-50426-7_8
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942016Magalhães, 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
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922016Shillcock, J.C., Hawrylycz, M., Hill, S., and Peng, H. (2016). Reconstructing the brain: from image stacks to neuron synthesis. Brain Inform 3, 205–209.
912016Van 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.
902016Vanherpe, L., Kanari, L., Atenekeng, G., Palacios, J., and Shillcock, J. (2016). Framework for efficient synthesis of spatially embedded morphologies. Phys Rev E 94, 023315.
892016Vanherpe, 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.
882016Wang, 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.
872015H. 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.
DOI:10.1016/j.cell.2015.09.029
862015S. 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.
DOI: 10.3389/fncir.2015.00044
852015M. Reimann, E.Muller, S.Ramaswamy, H.Markram: An Algorithm to Predict the Connectome of Neural Microcircuits. 2015. Frontiers in Neural Circuits 9 2015, 28.

DOI: 10.3389/fncom.2015.00120
842015A. 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.
DOI: 10.1145/2830168.2830172
832015 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.
DOI: 10.1145/2832087.2832088
822015M.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
DOI: 10.1186/1471-2105-16-S11-S8
812015V.Delattre, D.Keller, M.Perich, H.Markram, E.B.Muller: Network-timing-dependent plasticity. Front Cell Neurosci. 2015 Ju 9;9:220
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802015Anastassiou 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
792015S.Ramaswamy, H.Markram: Anatomy and Physiology of the thick-tufted layer 5 pyramidal neuron, Front Cell Neurosci. 2015; 9:233
DOI: 10.3389/fncel.2015.00233
782015D.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.
DOI: 10.1371/journal.pcbi.1004253
772015Costantini 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
762015Frackowiak 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
752015Vannucci, 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.
DOI: 10.1109/HUMANOIDS.2015.7363512
742015X.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
DOI: 10.3389/fnana.2015.00066
732015R.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
DOI: 10.1093/bioinformatics/btv025
722015S. Ramaswamy and E. Muller, Cell-type specific modulation of neocortical UP and DOWN states. Frontiers in Cellular Neuroscience, 9:370, 2015
DOI: 10.3389/fncel.2015.00370
712015S. Ramaswamy, Exciting times for inhibition: GABAergic synaptic transmission in dentate gyrus interneuron networks. Frontiers in Neural Circuits, 9:13, 2015.
DOI: 10.3389/fncir.2015.00013
702015 E. Muller, J. A. Bednar Diesmann M., M.-O., Gewaltig, M. Hines , A.P. Davison,. Python in Neuroscience. Frontiers in Neuroinformatics, 2015, 9,
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692015Wybo, 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
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682015Tiesinga, 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
672015Jolivet, 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.
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662015E.Hay and I.Segev: Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits. Cerebral Cortex, 2014 Sep 9
DOI: 10.1093/cercor/bhu200
652014F.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)
DOI: 10.1007/978-3-319-07518-1_21
642014T.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)
DOI: 10.1007/978-3-319-07518-1_29
632014S.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.
622014F.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.
DOI: 10.1109/ICDE.2014.6816718
612014MO.Gewaltig and R.Cannon: Current practice in software development for computational neuroscience and how to improve it. 2014. PLoS Comput Biol. 10(1).
DOI: 10.1371/journal.pcbi.1003376
602014J.DeFelipe, E.Garrido, H.Markram: The death of Cajal and the end of scientific romanticism and individualism. Trends Neurosci. 37(10):525-7 (2014)
DOI: 10.1016/j.tins.2014.08.002
592014Adaszewski, 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
582014Babai, 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.
DOI: 10.1523/JNEUROSCI.1990-14.2014
572014Kriener, 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
562014Toledo-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.]
Methods in Molecular Biology
DOI: 10.1007/978-1-4939-1096-0_8
552013M.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
542013E. 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
532013J.B. Hernando, J. Biddiscombe, B. Bohara, S. Eilemann, F. Schürmann: Practical parallel rendering of detailed neuron simulations, EGPGV 2013
DOI: 10.2312/EGPGV/EGPGV13/049-056
522013R.Perin, M.Telefont, H.Markram: Computing the size and number of neuronal clusters in local circuits, Front Neuroanat. 2013;7:1. Epub 2013 Feb 19.
DOI: 10.3389/fnana.2013.00001.
512013A.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.
DOI: 10.1523/JNEUROSCI.3740-12.2013
502013H.Markram: Seven challenges for Neuroscience. 2013. Functional Neurology. 28(3):145-51
DOI: 10.11138/FNeur/2013.28.3.144
492013ER.Kandel, H.Markram, PM.Matthews, R.Yuste, C.Koch: Neuroscience thinks big (and collaboratively). 2013. Nat Rev Neurosci. 14(9):659-64.
DOI: 10.1038/nrn3578
482013J.DeFelipe et al. [42 authors]: New insights into the classification and nomenclature of cortical GABAergic interneurons. 2013. Nat Rev Neurosci. 14(3):202-16.
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472013Wybo, 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
462013S.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
452012Markram, 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
442012Tauheed, 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
432012Hernando, 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
422012S.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
412012A.Gidon and I.Segev: Principles governing the operation of synaptic inhibition in dendrites, Neuron, 2012 Jul 26;75(2):330-41
DOI: 10.1016/j.neuron.2012.05.015
402012 F.Tauheed, T.Heinis, F.Schürmann, H.Markram, A.Ailamaki: SCOUT: Prefetching of Latent Structure Following Queries, VLDB 2012
DOI: 10.14778/2350229.2350267
392012G.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
382012S.Eilemann, A.Bilgili, M.Abdellah, J.Hernando, M.Makhinya, R.Pajarola, and F.Schürmann: Parallel Rendering on Hybrid Multi-GPU Clusters, EGPGV 2012
DOI: 10.2312/EGPGV/EGPGV12/109-117
372012S.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.
DOI: 10.1109/TVCG.2011.55
362012S.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.
DOI: 10.1113/jphysiol.2011.219576
352011R.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
342011M.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
332011E.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
322011S.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
312011R.Perin, T.K.Berger, and H.Markram: A synaptic organizing principle for cortical neuronal groups, PNAS, 2011, 108 (12)
DOI: 10.1073/pnas.1016051108
302011S.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
292011CA.Anastassiou, R.Perin, H.Markram, and C.Koch: Ephaptic coupling of cortical neurons, Nat Neurosci. 2011 Feb;14(2):217-23
DOI: 10.1038/nn.2727
282011H. Markram, W. Gerstner, PJ. Sjöström: A history of spike-timing-dependent plasticity. Front Synaptic Neurosci. 2011;3:4. Epub 2011 Aug 29.
DOI: 10.3389/fnsyn.2011.00004
272011H. Markram, R. Perin: Innate neural assemblies for lego memory. Front Neural Circuits. 2011;5:6. Epub 2011 May 16.
DOI: 10.3389/fncir.2011.00006
262010TK.Berger, G.Silberberg, R.Perin, and H.Markram: Brief bursts self-inhibit and correlate the pyramidal network, PLoS Biol. 2010 Sep 7;8(9)
DOI: 10.1371/journal.pbio.1000473
252010L.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.
242009A.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
DOI: 10.3389/neuro.10.027.2009
232009TK.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
DOI: 10.1113/jphysiol.2009.176552
222009J.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.
212009Anwar 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.
ISBN 978-0-262-01327
202008Jolivet, R., Schürmann, F., Berger, T. K., Naud, R., Gerstner, W., & Roth, A. (2008). The quantitative single-neuron modeling competition. Biological Cybernetics, 99(4), 417-426. https://doi.org/10.1007/s00422-008-0261-x
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
ISSN:0018-8646
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
DOI: 10.1007/s10827-007-0073-3
172008M.Hines, H.Markram, F.Schürmann: Fully Implicit Parallel Simulation of Single Neurons, J. Comput. Neurosci., 25(3):439-48, 2008
DOI: 10.1007/s10827-008-0087-5
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
DOI: 10.1007/s00422-008-0269-2
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.
DOI: 10.1007/s10827-007-0058-2
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.
DOI: 10.1007/s10827-008-0080-z
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
DOI: 10.1038/nrn2402
122008H.Markram: Fixing the location and dimensions of functional neocortical columns, HFSP J. 2008 Jun;2(3):132-5.
DOI: 10.2976/1.2919545
112007G.Silberberg and H.Markram: Disynaptic inhibition between neocortical pyramidal cells mediated by Martinotti cells, Neuron. 2007 Mar 1;53(5):735-46.
DOI: 10.1016/j.neuron.2007.02.012
102007H.Markram: Bioinformatics: industrializing neuroscience. Nature. 2007 Jan 11;445(7124):160-1.
DOI: 10.1038/445160a
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
DOI: 10.3389/neuro.01.1.1.001.2007
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.
DOI: 10.1007/978-1-59745-529-9_8
62007JV.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.
DOI: 10.1093/cercor/bhl127
52006M.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.
DOI: 10.1007/s10827-006-7949-5
42006H.Markram: The blue brain project. Nat Rev Neurosci. 7, 153-160, 2006
DOI: 10.1038/nrn1848
32006Y.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.
DOI: 10.1038/nn1670
22006JV.Le Bé and H.Markram: Spontaneous and evoked synaptic rewiring in the neonatal neocortex, PNAS. 2006 Aug 29;103(35):13214-9.
DOI: 10.1073/pnas.0604691103
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A.J. Muhammad, H. Markram, NEOBASE: Databasing the Neocortical Microcircuit, Stud Health Technol Inform. 2005;112:167-77.