Publication | Year | Authors |
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239 | 2023 | 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 |
238 | 2023 | 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 |
237 | 2023 | 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 |
236 | 2023 | 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 |
235 | 2022 | 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 |
234 | 2022 | 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 |
233 | 2022 | 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 |
232 | 2022 | 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 |
231 | 2022 | 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 |
230 | 2022 | 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 |
229 | 2022 | 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 |
228 | 2022 | 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 |
227 | 2022 | 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 |
226 | 2022 | 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 |
225 | 2022 | 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 |
224 | 2022 | 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 |
223 | 2022 | 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 |
222 | 2022 | 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 |
221 | 2022 | 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 |
220 | 2022 | 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. |
219 | 2022 | 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 |
218 | 2022 | 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 |
217 | 2022 | 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 |
216 | 2022 | 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 |
215 | 2022 | 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 |
214 | 2022 | 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 |
213 | 2022 | 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 |
212 | 2022 | 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 |
211 | 2022 | 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 |
210 | 2022 | 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 |
209 | 2022 | 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 |
208 | 2022 | 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 |
207 | 2022 | 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 |
206 | 2022 | 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 |
205 | 2022 | 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 |
204 | 2022 | 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 |
203 | 2022 | 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 |
202 | 2022 | 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 |
201 | 2022 | 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 |
200 | 2022 | 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 |
199 | 2021 | 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. |
198 | 2021 | 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 |
197 | 2021 | 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 |
196 | 2021 | 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 |
195 | 2021 | 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 |
194 | 2021 | 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 |
193 | 2021 | 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 |
192 | 2021 | 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. |
191 | 2021 | Gosztolai, 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. |
190 | 2021 | Isbister, 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 |
189 | 2021 | Logette, 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 |
188 | 2021 | Krepl, 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 |
187 | 2021 | 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 |
186 | 2021 | 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 |
185 | 2021 | 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 |
184 | 2021 | O’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 |
183 | 2021 | 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 |
182 | 2021 | 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. |
181 | 2021 | 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 |
180 | 2021 | 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 |
179 | 2020 | Magalhaes, 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. |
178 | 2020 | Kanari, 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. |
177 | 2020 | Ecker, 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. |
176 | 2020 | Ewart, 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. |
175 | 2020 | Abdellah, 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. |
174 | 2020 | Damart, 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 |
173 | 2020 | Magalhães, 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 |
172 | 2020 | Kumbhar, P., Awile, O., Keegan, L., Blanco Alonso, J., King, J., Hines, M., & Schürmann, F. (2020). An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language. In Krzhizhanovskaya, V. et al. (Eds.), ICCS 2020 Amsterdam: Lecture Notes in Computer Science, vol 12137. Springer, Cham. https://doi.org/10.1007/978-3-030-50371-0_4. |
171 | 2020 | Amsalem, O., King, J., Reimann, M., Ramaswamy, S., Muller, E., Markram, Nelken, H & Segev, I. (2020). Dense computer replica of cortical microcircuits unravels cellular underpinnings of auditory surprise response. bioRxiv, 2020.05.31. DOI: 10.1101/2020.05.31.126466 |
170 | 2020 | Gal, E., Perin, R., Markram, H., London, M., & Segev, I. (2020). Neuron geometry underlies universal network features in cortical microcircuits. bioRxiv, 2020.05.07. https://doi.org/10.1101/656058 |
169 | 2020 | Cremonesi, F., Hager, G., Wellein, G., and Schürmann, F. Analytic performance modeling and analysis of detailed neuron simulations April 3, 2020, The International Journal of High Performance Computing Applications. 34(4), 428–449. SAGE Publishing. DOI: 10.1177/1094342020912528 |
168 | 2020 | Dai, K., Hernando, J., Billeh, Y.N., Gratiy, S.L., Planas, J., Davison, A.P., Dura-Bernal, S., Gleeson, P., Devresse, A., Dichter, B.K., Gevaert, M., King, J.G., Van Geit, W.A.H., Povolotsky, A.V., Muller, E., Courcol, J.-D., and Arkhipov, A. (2020). The SONATA data format for efficient description of large-scale network models. PLOS Computational Biology, 16(2), e1007696. DOI: 10.1371/journal.pcbi.1007696. |
167 | 2020 | Coggan, J.S., Keller, D., Markram, H., Schürmann, F., and Magistretti, P.J. (2020). Excitation states of metabolic networks predict dose-response fingerprinting and ligand pulse phase signalling. Journal of Theoretical Biology, 487, 110123. DOI: 10.1016/j.jtbi.2019.110123. |
166 | 2020 | Cremonesi, F., and Schürmann, F. (2020). Understanding computational costs of cellular-level brain tissue simulations through analytical performance models. Neuroinformatics. 18, 407–428. DOI: 10.1007/s12021-019-09451-w. |
165 | 2020 | Nolte, M., Gal, E., Markram, H., and Reimann, M.W. (2020). Impact of higher-order network structure on emergent cortical activity. Network Neuroscience. 4(1), 292–314. DOI: 10.1162/netn_a_00124. |
164 | 2020 | Bryson, A., Hatch, R.J., Zandt, B.-J., Rossert, C., Berkovic, S.F., Reid, C.A., Grayden, D.B., Hill, S.L., and Petrou, S. (2020). GABA-mediated tonic inhibition differentially modulates gain in functional subtypes of cortical interneurons. Proceedings of the National Academy of Sciences, 117(6), 3192-3202. DOI: 10.1073/pnas.1906369117 |
163 | 2020 | Amsalem, O., Eyal, G., Rogozinski, N., Gevaert, M., Kumbhar,P., Schürmann, F., Segev, I. (2020). An efficient analytical reduction of detailed nonlinear neuron models. Nature Communications, 11(1), 288. DOI: 10.1038/s41467-019-13932-6 |
162 | 2019 | Karlsson, J., Abdellah, M., Foni, A., Lapere, S., and Schürmann, F. (2019). High fidelity visualization of large scale digitally reconstructed brain circuitry with signed distance functions. In 2019 IEEE Visualization Conference (VIS), 20-25 Oct. 2019, 176–180. DOI: 10.1109/VISUAL.2019.8933693. |
161 | 2019 | Magalhães, B.R.C., Sterling, T., Schürmann Felix, and Hines, M.L. (2019). Exploiting flow graph of system of odes to accelerate the simulation of biologically-detailed neural networks. In the proceeding of IEEE 2019 International Parallel and Distributed Processing Symposium (IPDPS), (Rio de Janeiro, Brazil), 176–187. doi.org/10.1109/IPDPS.2019.00028. |
160 | 2019 | Abdellah, M., Favreau, C., Hernando, J., Lapere, S., and Schürmann, F. (2019). Generating high fidelity surface meshes of neocortical neurons using skin modifiers. In Eurographics proceedings UK Computer Graphics & Visual Computing, F. Vidal, G. Tam, and J. Roberts, Eds. (Bangor University, Wales, UK: The Eurographics Association), 45–53. doi.org/10.2312/cgvc.20191257. |
159 | 2019 | Barros-Zulaica, N., Rahmon, J., Chindemi, G., Perin, R., Markram, H., Ramaswamy, S., and Muller, E. Estimating the readily-releasable vesicle pool size at layer 5 pyramidal connections in the neocortex. bioRxiv. 29 May 2019. https://www.biorxiv.org/content/10.1101/646497v1. |
158 | 2019 | Kumbhar, P., Hines, M., Fouriaux, J., Ovcharenko, A., King, J., Delalondre, F., and Schürmann, F. (2019). CoreNEURON : An optimized compute engine for the neuron simulator. Front. Neuroinform. 13, 63. DOI: 10.3389/fninf.2019.00063 |
157 | 2019 | Keller, D., Meystre, J., Veettil, R.V., Burri, O., Guiet, R., Schürmann, F., and Markram, H. (2019). A derived positional mapping of inhibitory subtypes in the somatosensory cortex. Front. Neuroanat. 13, 78. https://doi.org/10.3389/fnana.2019.00078 |
156 | 2019 | Reimann, M.W., Gevaert, M., Shi, Y., Lu, H., Markram, H., and Muller, E. A null model of the mouse whole-neocortex micro-connectome. Nature Communications 29 August 2019. https://doi.org/10.1038/s41467-019-11630-x |
155 | 2019 | Casalegno, F., Newton, T., Daher, R., Abdelaziz, M., Lodi-Rizzini, A., Schürmann, F., Krejci, I., and Markram, H. (2019). Caries Detection with Near-Infrared Transillumination Using Deep Learning. Journal of Dental Research. Online 26 August 2019. https://doi.org/10.1177/0022034519871884 |
154 | 2019 | Nolte M., Reimann M.W., King J., Markram H., Muller E., Cortical reliability amid noise and chaos. Nature Communications, 22 August 2019, https://doi.org/10.1038/s41467-019-11633-8 |
153 | 2019 | Ranjan R, Logette E, Marani M, Herzog M, Tâche V, Scantamburlo E, Buchillier V and Markram H. A Kinetic Map of the Homomeric Voltage-Gated Potassium Channel (Kv) Family. Front. Cell. Neurosci., 20 August 2019 | https://doi.org/10.3389/fncel.2019.00358 |
152 | 2019 | Magalhães, B.R.C., Sterling, T., Hines, M., and Schürmann, F. (2019). Asynchronous branch-parallel simulation of detailed neuron models. Frontiers in Neuroinformatics 13, 54. https://doi.org/10.3389/fninf.2019.00054 |
151 | 2019 | Gleeson, P., Cantarelli, M., Marin, B., Quintana, A., Earnshaw, M., Sadeh, S., Piasini, E., Birgiolas, J., Cannon, R.C., Cayco-Gajic, N.A., Crook, S., Davison, A.P., Dura-Bernal, S., Ecker, A., Hines, M.L., Idili, G., Lanore, F., Larson, S.D., Lytton, W.W., Majumdar, A., McDougal, R.A., Sivagnanam, S., Solinas, S., Stanislovas, R., van Albada, S.J., van Geit, W., and Silver, R.A. (2019). Open source brain: a collaborative resource for visualizing, analyzing, simulating, and developing standardized models of neurons and circuits. Neuron. Online 11 June 2019. https://doi.org/10.1016/j.neuron.2019.05.019. |
150 | 2019 | Wybo, W.A.M., Torben-Nielsen, B., Nevian, T., and Gewaltig, M.-O. (2019). Electrical compartmentalization in neurons. Cell reports 26, 1759-1773.e7. https://doi.org/10.1016/j.celrep.2019.01.074. |
149 | 2019 | Magalhães B.R.C., Sterling T., Hines M., Schürmann F. (2019) Fully-Asynchronous Cache-Efficient Simulation of Detailed Neural Networks. In: Rodrigues J. et al. (eds) Computational Science – ICCS 2019. ICCS 2019. Lecture Notes in Computer Science, vol 11538. Springer International Publishing, 421–434. doi.org/10.1007/978-3-030-22744-9_33 |
148 | 2019 | Iavarone E., Yi J., Shi Y., Zandt B.J., O’Reilly C., Van Geit W., Rössert C., Markram, H., Hill, S.L. (2019) Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons. PLOS Computational Biology 15(5): 1-23. e1006753. https://doi.org/10.1371/journal.pcbi.1006753 |
147 | 2019 | Einevoll, G.T., Destexhe, A., Diesmann, M., Grün, S., Jirsa, V., Kamps, M. de, Migliore, M., Ness, T.V., Plesser, H.E., Schürmann, F. (2019). The Scientific Case for Brain Simulations. Neuron 102, 735–744. https://doi.org/10.1016/j.neuron.2019.03.027 |
146 | 2019 | Fan 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 |
145 | 2019 | Colangelo, 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 |
144 | 2019 | Kanari, 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 |
143 | 2019 | Barros-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. |
142 | 2019 | Muddapu 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. DOI: 10.3389/fncir.2019.00011. |
141 | 2018 | Beche, A., De, K., Delalondre, F., Schuermann, F., Klimentov, A., & Mashinistov, R. (2018). Supercomputers, clouds and grids powered by BigPanDA for brain studies. Journal of Physics: Conference Series, 1085 (3), September2018. https://doi.org/10.1088/1742-6596/1085/3/032003. |
140 | 2018 | Tieck, J. C. V., Pogančić, M. V., Kaiser, J., Roennau, A., Gewaltig, M.-O., & Dillmann, R. (2018). Learning continuous muscle control for a multi-joint arm by extending proximal policy optimization with a liquid state machine. In V. Kůrková, Y. Manolopoulos, B. Hammer, L. Iliadis, & I. Maglogiannis (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2018 (pp. 211–221). Springer International Publishing. https://doi.org/10.1007/978-3-030-01418-6_21. |
139 | 2018 | Planas, 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 |
138 | 2018 | Abdellah, 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. |
137 | 2018 | Coggan, 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. |
136 | 2018 | Coggan, 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. |
135 | 2018 | Erö, 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. |
134 | 2018 | Eyal, 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. |
133 | 2018 | Johannes, 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. |
132 | 2018 | Kanari, 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. |
131 | 2018 | Keller, 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. |
130 | 2018 | Lindroos, 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. |
129 | 2018 | Migliore, 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. |
128 | 2018 | O’Reilly C., Chapotot F., Pittau F., Mella N., Picard F. (2018, accepted for publication). Nicotine increases sleep spindle activity. Journal of Sleep Research. |
127 | 2018 | Pezeshkian, 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. |
126 | 2018 | Ramaswamy, 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. |
125 | 2018 | Ramaswamy, 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. |
124 | 2018 | Shardlow, 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. |
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122 | 2017 | Abdellah, 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. |
121 | 2017 | Abdellah, 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. |
120 | 2017 | Abdellah, 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. |
119 | 2017 | Deitcher, 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. |
118 | 2017 | Doron, 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. |
117 | 2017 | Eilemann, 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. |
116 | 2017 | Ewart, 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. |
115 | 2017 | Falotico, 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. |
114 | 2017 | Gal, 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. |
113 | 2017 | Hinkel, 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. |
112 | 2017 | Masoli, 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. |
111 | 2017 | O’Reilly, C., Iavarone, E., and Hill, S.L. (2017). A Framework for Collaborative Curation of Neuroscientific Literature. Front Neuroinform 11, 27. |
110 | 2017 | Podlaski, 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. |
109 | 2017 | Ramaswamy, S., Colangelo, C., and Muller, E.B. (2017). Distinct Activity Profiles of Somatostatin-Expressing Interneurons in the Neocortex. Front Cell Neurosci 11, 273. |
108 | 2017 | Reimann, 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. |
107 | 2017 | Reimann, 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. |
106 | 2017 | Rössert, 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. |
105 | 2017 | Schumann, 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 |
104 | 2016 | Ambrosano, 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. |
103 | 2016 | Amsalem, 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. |
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100 | 2016 | Halnes, 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. |
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97 | 2016 | Kumbhar, 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. |
96 | 2016 | Leitner, F., Bielza, C., Hill, S.L., and Larrañaga, P. (2016). Data Publications Correlate with Citation Impact. Front Neurosci 10, 419. |
95 | 2016 | Lytton, 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. |
94 | 2016 | Magalhães, 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. |
93 | 2016 | Roehrbein, 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. |
92 | 2016 | Shillcock, J.C., Hawrylycz, M., Hill, S., and Peng, H. (2016). Reconstructing the brain: from image stacks to neuron synthesis. Brain Inform 3, 205–209. |
91 | 2016 | Van 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. |
90 | 2016 | Vanherpe, L., Kanari, L., Atenekeng, G., Palacios, J., and Shillcock, J. (2016). Framework for efficient synthesis of spatially embedded morphologies. Phys Rev E 94, 023315. |
89 | 2016 | Vanherpe, 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. |
88 | 2016 | Wang, 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. |
87 | 2015 | H. 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 |
86 | 2015 | S. 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 |
85 | 2015 | M. 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 |
84 | 2015 | A. 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 |
83 | 2015 | 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 |
82 | 2015 | M.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 |
81 | 2015 | V.Delattre, D.Keller, M.Perich, H.Markram, E.B.Muller: Network-timing-dependent plasticity. Front Cell Neurosci. 2015 Ju 9;9:220 DOI: 10.3389/fncel.2015.00220 |
80 | 2015 | Anastassiou 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 |
79 | 2015 | S.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 |
78 | 2015 | D.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 |
77 | 2015 | Costantini 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 |
76 | 2015 | Frackowiak 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 |
75 | 2015 | 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., 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 |
74 | 2015 | X.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 |
73 | 2015 | R.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 |
72 | 2015 | S. 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 |
71 | 2015 | S. 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 |
70 | 2015 | 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 |
69 | 2015 | Wybo, 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 DOI: 10.1162/NECO_a_00788 |
68 | 2015 | Tiesinga, 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 |
67 | 2015 | Jolivet, 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 |
66 | 2015 | E.Hay and I.Segev: Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits. Cerebral Cortex, 2014 Sep 9 DOI: 10.1093/cercor/bhu200 |
65 | 2014 | F.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 |
64 | 2014 | T.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 |
63 | 2014 | S.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. |
62 | 2014 | F.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 |
61 | 2014 | MO.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 |
60 | 2014 | J.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 |
59 | 2014 | Adaszewski, 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 |
58 | 2014 | Babai, 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 |
57 | 2014 | Kriener, 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 |
56 | 2014 | Toledo-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 |
55 | 2013 | M.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 |
54 | 2013 | E. 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 |
53 | 2013 | J.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 |
52 | 2013 | R.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. |
51 | 2013 | A.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 |
50 | 2013 | H.Markram: Seven challenges for Neuroscience. 2013. Functional Neurology. 28(3):145-51 DOI: 10.11138/FNeur/2013.28.3.144 |
49 | 2013 | ER.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 |
48 | 2013 | J.DeFelipe et al. [42 authors]: New insights into the classification and nomenclature of cortical GABAergic interneurons. 2013. Nat Rev Neurosci. 14(3):202-16. DOI: 10.1038/nrn3444 |
47 | 2013 | Wybo, 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 |
46 | 2013 | S.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 |
45 | 2012 | Markram, 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 |
44 | 2012 | Tauheed, 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 |
43 | 2012 | Hernando, 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 |
42 | 2012 | S.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 |
41 | 2012 | A.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 |
40 | 2012 | F.Tauheed, T.Heinis, F.Schürmann, H.Markram, A.Ailamaki: SCOUT: Prefetching of Latent Structure Following Queries, VLDB 2012 DOI: 10.14778/2350229.2350267 |
39 | 2012 | G.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 |
38 | 2012 | S.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 |
37 | 2012 | S.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 |
36 | 2012 | S.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 |
35 | 2011 | R.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 |
34 | 2011 | M.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 |
33 | 2011 | E.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 |
32 | 2011 | S.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 |
31 | 2011 | R.Perin, T.K.Berger, and H.Markram: A synaptic organizing principle for cortical neuronal groups, PNAS, 2011, 108 (12) DOI: 10.1073/pnas.1016051108 |
30 | 2011 | S.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 |
29 | 2011 | CA.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 |
28 | 2011 | H. 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 |
27 | 2011 | H. 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 |
26 | 2010 | TK.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 |
25 | 2010 | L.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. |
24 | 2009 | A.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 |
23 | 2009 | TK.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 |
22 | 2009 | J.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. |
21 | 2009 | Anwar 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 |
20 | 2008 | Jolivet, 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 |
19 | 2008 | J.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 |
18 | 2008 | M.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 |
17 | 2008 | M.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 |
16 | 2008 | S.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 |
15 | 2008 | C.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 |
14 | 2008 | O.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 |
13 | 2008 | GA.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 |
12 | 2008 | H.Markram: Fixing the location and dimensions of functional neocortical columns, HFSP J. 2008 Jun;2(3):132-5. DOI: 10.2976/1.2919545 |
11 | 2007 | G.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 |
10 | 2007 | H.Markram: Bioinformatics: industrializing neuroscience. Nature. 2007 Jan 11;445(7124):160-1. DOI: 10.1038/445160a |
9 | 2007 | A.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 |
8 | 2007 | S.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 |
7 | 2007 | M.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 |
6 | 2007 | JV.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 |
5 | 2006 | M.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 |
4 | 2006 | H.Markram: The blue brain project. Nat Rev Neurosci. 7, 153-160, 2006 DOI: 10.1038/nrn1848 |
3 | 2006 | Y.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 |
2 | 2006 | JV.Le Bé and H.Markram: Spontaneous and evoked synaptic rewiring in the neonatal neocortex, PNAS. 2006 Aug 29;103(35):13214-9. DOI: |
1 | 2005 | A.J. Muhammad, H. Markram, NEOBASE: Databasing the Neocortical Microcircuit, Stud Health Technol Inform. 2005;112:167-77. |