Publications

231. 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

230. 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

229. 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

228. 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

227. 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

226. 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

225. 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

224. 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

223. 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

222. 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

221. 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

220. 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

219. 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.

218. 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

217. 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

216. 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

215. 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

214. 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

213. 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

212. 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

211. 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

210. 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

209. 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

208. 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

207. 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

206. 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

205. 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

204. 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

203. 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

202. 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

201. 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

200. 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

199. 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

198. 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.

197. 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

196. 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

195. 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

194.  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

193. 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

192. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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., & 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. 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. 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. Magalhaes, B., & Schürmann, F. (2020). Efficient distributed transposition of large-scale multigraphs and high-cardinality sparse matrices. arXiv, 10December2020. http://arxiv.org/abs/2012.06012.

178. 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. 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. Ewart, T., Cremonesi, F., Schürmann, F., & Delalondre, F. (2020). Polynomial evaluation on superscalar architecture, applied to the elementary function e^x. ACM Transactions on Mathematical Software, 46(3). Association for Computing Machinery. https://doi.org/10.1145/3408893.

175. 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. 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. 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. 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. 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. Gal, E., Perin, R., Markram, H., London, M., & Segev, I. (2020). Neuron geometry underlies universal network features in cortical microcircuits. bioRxiv, 2020.05.07.
DOI: https://doi.org/10.1101/656058

169. Cremonesi, F., Hager, G., Wellein, G., & 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. 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., & 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. Coggan, J.S., Keller, D., Markram, H., Schürmann, F., & 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. Cremonesi, F., & 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. Nolte, M., Gal, E., Markram, H., & 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. Bryson, A., Hatch, R.J., Zandt, B.-J., Rossert, C., Berkovic, S.F., Reid, C.A., Grayden, D.B., Hill, S.L., & 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. Amsalem, O., Eyal, G., Rogozinski, N., Gevaert, M., Kumbhar,P., Schürmann, F., & Segev, I. An efficient analytical reduction of detailed nonlinear neuron models. Nat Commun 11, 288
DOI: 10.1038/s41467-019-13932-6

140. 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.

139. 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.

138. Planas, J., Delalondre, F., & 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

137. Abdellah, M., Hernando, J., Eilemann, S., Lapere, S., Antille, N., Markram, H., & Schürmann, F. (2018). NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks. Bioinformatics. 34, i574–i582.
DOI: 10.1093/bioinformatics/bty231.

136. 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.
DOI: 10.3389/fnins.2018.00664.

135. Coggan, J.S., Keller, D., Calì, C., Lehväslaiho, H., Markram, H., Schürmann, F., & Magistretti, P.J. (2018b). Norepinephrine stimulates glycogenolysis in astrocytes to fuel neurons with lactate. PLOS Computational Biology. 14(8).
DOI: 10.1371/journal.pcbi.1006392.

134. Erö, C., Gewaltig, M.-O., Keller, D., & Markram, H. (2018). A Cell Atlas for the Mouse Brain. Frontiers in Neuroinformatics. 12, 84.
DOI: 10.3389/fninf.2018.00084

133. Eyal, G., Verhoog, M.B., Testa-Silva, G., Deitcher, Y., Benavides-Piccione, R., DeFelipe, J., de Kock, C.P.J., Mansvelder, H.D., & Segev, I. (2018). Human Cortical Pyramidal Neurons: From Spines to Spikes via Models. Frontiers in Cellular Neuroscience. 12, 181.
DOI: 10.3389/fncel.2018.00181.

132. Johannes, L., Pezeshkian, W., Ipsen, J.H., & 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.

131. Kanari, L., Dłotko, P., Scolamiero, M., Levi, R., Shillcock, J., Hess, K., & 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.

130. Keller, D., Erö, C., & Markram, H. (2018). Cell Densities in the Mouse Brain: A Systematic Review. Front Neuroanat. 12, 83.
DOI: 10.3389/fnana.2018.00083.

129. 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.
DOI: 10.3389/fncir.2018.00003.

128. 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).
DOI: 10.1371/journal.pcbi.1006423.

127. Pezeshkian, W., Gao, H., Arumugam, S., Becken, U., Bassereau, P., Florent, J.-C., Ipsen, J.H., Johannes, L., & Shillcock, J.C. (2018). Mechanism of Shiga Toxin Clustering on Membranes. ACS Nano. 12, 2079–2079.
DOI: 10.1021/acsnano.8b00537.

126. 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://www.frontiersin.org/articles/10.3389/fncir.2018.00077/full

125. Ramaswamy, S., Muller, E., Reimann, M., & 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. Shardlow, M., Ju, M., Li, M., O’Reilly, C., Iavarone, E., McNaught, J., & 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.
DOI: 10.1007/s12021-018-9404-y.

123. Abdellah, M., Bilgili, A., Eilemann, S., Markram, H., & 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.

122. Abdellah, M., Bilgili, A., Eilemann, S., Shillcock, J., Markram, H., & Schürmann, F. (2017). Bio-physically plausible visualization of highly scattering fluorescent neocortical models for in silico experimentation. BMC Bioinformatics. 18, 62.
DOI: 10.1186/s12859-016-1444-4

121. Abdellah, M., Hernando, J., Antille, N., Eilemann, S., Markram, H., & Schürmann, F. (2017). Reconstruction and visualization of large-scale volumetric models of neocorticalcircuits for physically-plausible in silico optical studies. BMC Bioinformatics. 18, 402.
DOI: 10.1186/s12859-017-1788-4

120. Deitcher, Y., Eyal, G., Kanari, L., Verhoog, M.B., Atenekeng Kahou, G.A., Mansvelder, H.D., de Kock, C.P.J., & 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.
DOI: 10.1093/cercor/bhx226

119. Doron, M., Chindemi, G., Muller, E., Markram, H., & Segev, I. (2017). Timed SynapticInhibition Shapes NMDA Spikes, Influencing Local Dendritic Processing and Global I/OProperties of Cortical Neurons. Cell Rep 21, 1550–1561.
DOI: 10.1016/j.celrep.2017.10.035

118. 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.
DOI: 10.1007/978-3-319-67630-2_47

117. Ewart, T., Planas, J., Cremonesi, F., Langen, K., Schürmann, F., & 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.
DOI: 10.1007/978-3-319-58667-0_10

116. 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.
DOI: 10.3389/fnbot.2017.00002

115. Gal, E., London, M., Globerson, A., Ramaswamy, S., Reimann, M.W., Muller, E., Markram, H., & Segev, I. (2017). Rich cell-type-specific network topology in neocortical microcircuitry. Nat. Neurosci. 20, 1004–1013.
DOI: 10.1038/nn.4576

114. 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.
DOI: 10.1007/s10846-016-0412-6

113. Masoli, S., Rizza, M.F., Sgritta, M., Van Geit, W., Schürmann, F., & 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.
DOI: 10.3389/fncel.2017.00071

112. O’Reilly, C., Iavarone, E., & Hill, S.L. (2017). A Framework for Collaborative Curation of Neuroscientific Literature. Front Neuroinform. 11, 27.
DOI: 10.3389/fninf.2017.00027

111. Podlaski, W.F., Seeholzer, A., Groschner, L.N., Miesenböck, G., Ranjan, R., & Vogels, T.P. (2017). Mapping the function of neuronal ion channels in model and experiment. Elife 6.
DOI: 10.7554/eLife.22152

110. Ramaswamy, S., Colangelo, C., & Muller, E.B. (2017). Distinct Activity Profiles of Somatostatin-Expressing Interneurons in the Neocortex. Front Cell Neurosci. 11, 273.
DOI: 10.3389/fncel.2017.00273

109. Reimann, M.W., Horlemann, A.-L., Ramaswamy, S., Muller, E.B., & Markram, H. (2017). Morphological Diversity Strongly Constrains Synaptic Connectivity and Plasticity. Cereb.Cortex 27, 4570–4585.
DOI: 10.1093/cercor/bhx150

108. Reimann, M.W., Nolte, M., Scolamiero, M., Turner, K., Perin, R., Chindemi, G., Dłotko, P., Levi, R., Hess, K., & Markram, H. (2017). Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function. Front Comput Neurosci. 11, 48.
DOI: 10.3389/fncom.2017.00048

107. 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.

106. Schumann, T., Erő, C., Gewaltig, M.-O., & 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

105. 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.
DOI: 10.1007/978-3-319-42417-0_2

104. Amsalem, O., Van Geit, W., Muller, E., Markram, H., & 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.
DOI: 10.1093/cercor/bhw166

103. Eilemann, S., Delalondre, F., Bernard, J., Planas, J., Schuermann, F., Biddiscombe, J., Bekas, C., Curioni, A., Metzler, B., Kaltstein, P., Morjan, P., Fenkes, J., Bellofatto, R., Schneidenbach, L., Ward, T.J.C., & Fitch, B.G. (2016). Key/Value-Enabled Flash Memory for Complex Scientific Workflows with On-Line Analysis and Visualization. In IEEE International Parallel and Distributed Processing Symposium (IPDPS), (IEEE), pp. 608–617.
DOI: 10.1109/IPDPS.2016.23

102. Eyal G., Verhoog M. B., Testa-Silva G., Deitcher Y., Lodder J. C., Benavides-Piccione R., Morales J., DeFelipe J., de Kock C. P.J., Mansvelder H. D., & Segev I. (2016). Unique membrane properties and enhanced signal processing in human neocortical neurons. eLife 6;5. pii: e16553.
DOI: 10.7554/eLife.16553

101. Halnes, G., Mäki-Marttunen, T., Keller, D., Pettersen, K.H., Andreassen, O.A., & Einevoll, G.T. (2016). Effect of Ionic Diffusion on Extracellular Potentials in Neural Tissue. PLoS Comput. Biol. 12, e1005193.
DOI: 10.1371/journal.pcbi.1005193

100. Hill S., How do we know what we know? Discovering neuroscience data sets through minimal metadata (2016). Nat Rev Neurosci. 12, 735.
DOI: doi.org/10.1038/nrn.2016.134

99. Knoll A., & Gewaltig M-O., Neurorobotics: A strategic pillar of the Human Brain Project (2016). Chapter in Supplement to Science. Robotics in Brain- inspired intelligent robotics: The intersection of robotics and neuroscience.
(Science/AAAS, Washington, DC, 2016), p. 25-34.

98. Kumbhar, P., Hines, M., Ovcharenko, A., Mallon, D.A., King, J., Sainz, F., Schürmann, F., & 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.
DOI: 10.1007/978-3-319-41321-1_19

97. Leitner, F., Bielza, C., Hill, S.L., & Larrañaga, P. (2016). Data Publications Correlate with Citation Impact. Front Neurosci. 10, 419.
DOI: 10.3389/fnins.2016.00419

96. Lytton, W.W., Seidenstein, A.H., Dura-Bernal, S., McDougal, R.A., Schürmann, F., & Hines, M.L. (2016). Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON. Neural Comput. 28, 2063–2090.
DOI: 10.1162/NECO_a_00876

95. Magalhães, B.R.C., Tauheed, F., Heinis, T., Ailamaki, A., & 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.
DOI: 10.1007/978-3-319-41321-1_5

94. Roehrbein, F., Gewaltig, M.-O., Laschi, C., Klinker, G., Levi, P., & 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.

93. Shillcock, J.C., Hawrylycz, M., Hill, S., & Peng, H. (2016). Reconstructing the brain: from image stacks to neuron synthesis. Brain Inform. 3, 205–209.
DOI: 10.1007/s40708-016-0041-7

92. Van Geit, W., Gevaert, M., Chindemi, G., Rössert, C., Courcol, J.-D., Muller, E.B., Schürmann, F., Segev, I., & Markram, H. (2016). BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience. Front Neuroinform. 10.
DOI: 10.3389/fninf.2016.00017

91. Vanherpe, L., Kanari, L., Atenekeng, G., Palacios, J., & Shillcock, J. (2016). Framework for efficient synthesis of spatially embedded morphologies. Phys Rev. E 94, 023315.
DOI: 10.1103/PhysRevE.94.023315

90. Vanherpe, L., Kanari, L., Atenekeng, G., Palacios, J., & 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.
DOI: 10.1007/s12572-015-0156-8

89. 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, DOI:  10.1093/database/baw119.

88. Peng, H., Hawrylycz, M., Roskams, J., Hill, S., Spruston, N., Meijering, E., & Ascoli, G., A. (2015). BigNeuron: Large-scale 3D neuron reconstruction from optical microscopy images. Neuron, 87(2), 252–256. https://doi.org/10.1016/j.neuron.2015.06.036

87. Markram, H., Muller, E., Ramaswamy, S., Reimann, M., Abdellah, M., Sanchez, C., A., Ailamaki, A.,  Alonso-Nanclares, L., Antille, N., Arsever, S., Kahou, G., A., A., Berger, T., K., Bilgili, A.,  Buncic, N.,  Chalimourda, A., Chindemi, G., Courcol, J.-D., Delalondre, F., Delattre, V., Druckmann, S., Dumusc, R., Dynes, J., Eilemann, S., Gal, E., Gevaert, M., E., Ghobril, J.,-P., Gidon, A., Graham, J., W., Gupta, A., Haenel, V., Hay, E., Heinis, T., Hernando, J., B., Hines, M., Kanari, L., Keller, D., Kenyon, J., Khazen, G.,  Kim, Y., King, J., G., Kisvarday, Z., Kumbhar, P., Lasserre, S., Le Bé, J.,-V., Magalhães, B., R.,C.,  Merchán-Pérez, A., Meystre, J., Morrice, B.,R., Muller, J., Muñoz-Céspedes, A., Muralidhar, S., Muthurasa, K., Nachbaur, D., Newton, T., H., Nolte, M., Ovcharenko, A., Palacios, J., Pastor, L., Perin, R., Ranjan, R., Riachi, I., Rodríguez, J.,-R., Riquelme, J., L., Rössert, C., Sfyrakis, K., Shi, Y., Shillcock, J., C., Silberberg, G., Silva, R., Tauheed, F., Telefont, M., Toledo-Rodriguez, M., Tränkler, T., Van Geit, W.,  Díaz, J., V., Walker, R., Wang, Y., Zaninetta, S., M., DeFelipe, J., Hill, S., L., Segev, I., & Schürmann, F. (2015). Reconstruction and Simulation of Neocortical Microcircuitry. Cell, 163, 456-492.
DOI: 10.1016/j.cell.2015.09.029

86. Ramaswamy, S., Courcol, J.,-D., Abdellah, M., Adaszewski, S., R., Antille, N., Arsever, S., Atenekeng, G., Bilgili, A., Brukau, Y., Chalimourda, A., Chindemi, G., Delalondre, F., Dumusc, R., Eilemann, S.,  Gevaert, M., E., Gleeson, P., Graham, J., W., Hernando, J., B., Kanari, L., Katkov, Y., Keller, D., King, J., G., Ranjan, R., Reimann, M., W., Rössert, C., Shi, Y., Shillcock, J., C., Telefont, M., Van Geit, W.,  Diaz, J., V., Walker, R., Wang, Y., Zaninetta, S., M., DeFelipe, J., Hill, S., L., Muller, J., Segev, I., Schürmann, F., Muller, E., B., & Markram, H. (2015). The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex. Front. Neural Circuits, 44.
DOI: 10.3389/fncir.2015.00044

85. Reimann, M., Muller, E., Ramaswamy, S., & Markram H. (2015). An Algorithm to Predict the Connectome of Neural Microcircuits. 2015. Frontiers in Neural Circuits 9  28.
DOI: 10.3389/fncom.2015.00120

84. Devresse, A., Delalondre, F., & Schürmann, F. (2015). 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 Austin, Texas.
DOI: 10.1145/2830168.2830172

83. Ewart, T., Yates, S., Cremonesi, F., Kumbhar, P., Schuermann, F., & Delalondre, F. 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. Abdellah, M., Bilgilli, A., Eilemann, S., Markram, H., & Schürmann F. (2015) Physically-based in silico light sheet microscopy for visualizing fluorescent brain models. BMC Bioinformatics. Aug 13;16 Suppl 11:S8
DOI: 10.1186/1471-2105-16-S11-S8

81. Delattre, V., Keller, D., Perich, M., Markram, H., &  Muller E., B. (2015). Network-timing-dependent plasticity. Front Cell Neurosci. Ju 9;9:220.
DOI: 10.3389/fncel.2015.00220

80. Anastassiou C., A., Perin R., Buzsáki G., & Markram H., & Koch C. (2015). Cell type- and activity-dependent extracellular correlates of intracellular spiking. J Neurophysiol. Jul;114(1):608-23.
DOI: 10.1152/jn.00628.2014

79. Ramaswamy, S., & Markram H. (2015). Anatomy and Physiology of the thick-tufted layer 5 pyramidal neuron, Front Cell Neurosci. 9:233.
DOI: 10.3389/fncel.2015.00233

78. Keller, D., Babai, N., Kochubey, O., Han, Y., Markram, H., Schürmann, F., & Schneggenburger R. (2015). An Exclusion Zone for Ca2+ Channels around Docked Vesicles Explains Release Control by Multiple Channels at a CNS Synapse, PLoS Comput Biol. May 7;11(5):e1004253.
DOI: 10.1371/journal.pcbi.1004253

77. Costantini I., Ghobril J., P., Di Giovanna A., P., Allegra Mascaro A., L., Silvestri L., Müllenbroich M., C., Onofri L., Conti V., Vanzi F., Sacconi L., Guerrini R., Markram H., Iannello G., & Pavone F., S. (2015).  A versatile clearing agent for multi-modal brain imaging. Scientific Reports. May 7;5:9808.
DOI: 10.1038/srep09808.

76. Frackowiak R., & Markram H. (2015). The future of human cerebral cartography: a novel approach. Philos Trans R Soc Lond B Biol Sci. May 19;370(1668). pii: 20140171.
DOI: 10.1098/rstb.2014.0171.

75. Vannucci, L., Ambrosano, A., Cauli, N., Albanese, U., Falotico, E., Ulbrich, S., Pfotzer, L., Hinkel, G., Denninger, O., Peppicelli, D., Guyot, L., Von Arnim, A., Deser, S., Maier, P., Dillman, R., Klinker, G., Levi, P., Knoll, A., Gewaltig, M.-O., & Laschi, C. (2015). A visual tracking model implemented on the iCub robot as a use case for a novel neurorobotic toolkit integrating brain and physics simulation. In 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 1179–1184.
DOI: 10.1109/HUMANOIDS.2015.7363512

74. Vasques, X., Richardet, R., Hill, S.,L., Slater, D., Chappelier, J.,-C., Pralong, E., Bloh, J., Draganski, B., & Cif L. (2015). Automatic target validation based on neuroscientific literature mining for tractography, Front Neuroanat. May 27;9:66.
DOI: 10.3389/fnana.2015.00066

73. Richardet, R., Chappelier, J.,-C., Telefont, M., & Hill S. (2015). Large-scale extraction of brain connectivity from the neuroscientific literature, Bioinformatics. May; 31(10):1640-1647.
DOI: 10.1093/bioinformatics/btv025

72. Ramaswamy, S., & Muller, E. (2015).  Cell-type specific modulation of neocortical UP and DOWN states. Frontiers in Cellular Neuroscience, 9:370,
DOI: 10.3389/fncel.2015.00370

71. Ramaswamy, S. (2015). Exciting times for inhibition: GABAergic synaptic transmission in dentate gyrus interneuron networks. Frontiers in Neural Circuits, 9:13,
DOI: 10.3389/fncir.2015.00013

70. Muller, E., Bednar, J., A., Diesmann M.,  Gewaltig, M.-O., Hines, M., & Davison, A., P., (2015). Python in Neuroscience. Frontiers in Neuroinformatics, 9.
DOI: 10.3389/fninf.2015.00011

69. Wybo, W., A., M., Boccalini, D., Torben-Nielsen, B., & 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.
DOI: 10.1162/NECO_a_00788

68. Tiesinga, P., Bakker, R., Hill, S., & Bjaalie, J.G. (2015). Feeding the human brain model. Curr. Opin. Neurobiol. 32, 107–114.
DOI: doi:10.1016/j.conb.2015.02.003

67. Jolivet, R., Coggan, J.S., Allaman, I., & Magistretti, P.J. (2015). Multi-ti66. E.Hay and I.Segev: Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits. Cerebral Cortex, 2014 Sep 9.
DOI: 10.1093/cercor/bhu200

66. Hay, E., & Segev, I. (2015). Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits. Cerebral Cortex, 2014 Sep 9
DOI: 10.1093/cercor/bhu200

65. Schürmann, F., Delalondre, F., Kumbhar, P.,S., Biddiscombe, J., Gila, M., Tacchella, D., Curioni, A., Metzler, B., Morjan, P., Fenkes, J., Franceschini, M., M., Germain, R., S., Schneidenbach, L., Ward, T., J., C., & Fitch B., G., 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. Ewart, T., Delalondre, F., & Schürmann F., 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. Muralidhar, S., Wang, Y., & Markram H. 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

62. Tauheed, F.,Heinis, T., Schürmann, F., Markram, H., & Ailamaki A. 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. Gewaltig, MO., & Cannon, R. 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. DeFelipe, J., Garrido, E., & Markram H. 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. 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. Babai, N., Kochubey, O., Keller, D., & 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. Kriener, B., Enger, H., Tetzlaff, T., Plesser, H.E., Gewaltig, M.-O., & 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. Toledo-Rodriguez, M., & 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.]
DOI: 10.1007/978-1-4939-1096-0_8

55. Reimann, M., W., Anastassiou, C., A., Perin, R., Hill, S., L., Markram, H.,  & Koch C. A biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents. Neuron, 79(2), 375-390, 2013.
DOI: 0.1016/j.neuron.2013.05.023

54. Hay, E., Schürmann, F., Markram, H., & Segev, I. Preserving axosomatic spiking features despite diverse dendritic morphology. J Neurophysiol, 109(12), 2972-2981, 2013.
DOI: 10.1152/jn.00048.2013

53. Hernando, J., B., Biddiscombe, J., Bohara, B., Eilemann, S., & Schürmann F. Practical parallel rendering of detailed neuron simulations, EGPGV. 2013.
DOI: 10.2312/EGPGV/EGPGV13/049-056

52. Perin, R., Telefont, M., & Markram H.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. Loebel, A., LeBe, JV., Richardson, MJ., Markram, H., & Herz A. 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. Markram H. Seven challenges for Neuroscience. 2013. Functional Neurology. 28(3):145-51.
DOI: 10.11138/FNeur/2013.28.3.145

49. Kandel, ER., Markram, H., Matthews, PM., Yuste, & Koch C. Neuroscience thinks big (and collaboratively). 2013. Nat Rev Neurosci. 14(9):659-64.
DOI: 10.1038/nrn3578

48. 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. Wybo, W.A.M., Stiefel, K.M., & 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. Druckmann, S., Hill, S., Schürmann, F., Markram, H., & Segev I. A Hierarchical Structure of Cortical Interneuron Electrical Diversity Revealed by Automated Statistical Analysis, Cerebral Cortex, (2012), doi: 10.1093/cercor/bhs290.
DOI: 10.1093/cercor/bhs290

45 Markram, H., Gerstner, W., & 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. Tauheed, F., Biveinis, L., Heinis, T., Schurmann, F., Markram, H., & 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. Hernando, J., Schürmann, F., & 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. Hill, S., L., Wang, Y., Riachi, I., Schürmann, F., & Markram H. 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. Gidon A., & Segev I. 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. Tauheed, F., Heinis, Schürmann, F.,Markram, H., & SCOUT A., A. Prefetching of Latent Structure Following Queries, VLDB 2012.
DOI: 10.14778/2350229.2350267

39. Khazen, G.,Hill, S., L., Schürmann F., & Markram H. 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. Eilemann, S., Bilgili, A., Abdellah, M., Hernando, J., Makhinya, M., Pajarola, R., & Schürmann F. Parallel Rendering on Hybrid Multi-GPU Clusters, EGPGV 2012.
DOI: 10.2312/EGPGV/EGPGV12/109-117

37. Lasserre, S., Hernando, J., Hill, S., Schürmann, F., de Miguel Anasagasti, P., Abou Jaoudé, G., & Markram H. 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

35. Ranjan, R., Khazen, G., Gambazzi, L., Ramaswamy, S., Hill, S., L., Schürmann, F., & Markram H. Channelpedia: an integrative and interactive database for ion channels, Front. Neuroinform. 2011. 5:36.
DOI: 10.3389/fninf.2011.00036

34. Hines, M., Kumar, S., & Schürmann F. 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. Hay, E., Hill, S.,L., Schürmann, F., Markram, H., & Segev I. 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. Druckmann, S., Berger, T.,K., Schürmann, F., Hill, S., L., Markram, H., & Segev I., Effective stimuli for constructing reliable neuron models, Plos Computational Biology, 2011, 7(8): e1002133.
DOI: 10.1371/journal.pcbi.1002133

31. Perin, R., Berger, T., K., & Markram H. A synaptic organizing principle for cortical neuronal groups, PNAS, 2011, 108 (12).
DOI: 10.1073/pnas.1016051108

30. Romand, S., Wang, Y., Toledo-Rodriguez, M., & Markram H. 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. Anastassiou, CA., Perin, R., Markram, H., & Koch C. Ephaptic coupling of cortical neurons, Nat Neurosci. 2011 Feb;14(2):217-23.
DOI: 10.1038/nn.2727

28. Markram, H., Gerstner, W., & Sjöström PJ. A history of spike-timing-dependent plasticity. Front Synaptic Neurosci. 2011;3:4. Epub 2011 Aug 29.
DOI: 10.3389/fnsyn.2011.00004

27. Markram, H., & Perin R. Innate neural assemblies for lego memory. Front Neural Circuits. 2011;5:6. Epub 2011 May 16.
DOI: 10.3389/fncir.2011.00006

26. Berger, TK., Silberberg, G., Perin, R., & Markram H. Brief bursts self-inhibit and correlate the pyramidal network, PLoS Biol. 2010 Sep 7;8(9).
DOI: 10.1371/journal.pbio.1000473

25. Bar-Ilan, L., Gidon, A., & Segev I. Inter-regional synaptic competition in neurons with multiple STDP-inducing signals, J Neurophysiol (December 1, 2010).
DOI: 10.1152/jn.00612.2010.

24. Loebel, A., Silberberg, G., Helbig, D., Markram, H., Tsodyks, M., & Richardson MJ. 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. Berger, TK. Perin, R., Silberberg, G., & Markram H. 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. King, J., G., Hines, M., Hill, S., Goodman, P., H., Markram, H., & Schürmann F. A component-based extension framework for large-scale parallel simulations in NEURON , Front Neuroinformatics, 3:10.
DOI: 10.3389/neuro.11.010.2009

21. 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.  https://mitpress.mit.edu/books/computational-modeling-methods-neuroscientists.
ISBN 978-0-262-01327

20. 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. Kozloski, J., Sfyrakis, K., Hill, S., Schürmann, F., Peck, C., & Markram H. 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. Hines, M., Eichner, H., & Schürmann F. 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. Hines, M., Markram, H., & Schürmann F. Fully Implicit Parallel Simulation of Single Neurons, J. Comput. Neurosci., 25(3):439-48, 2008.
DOI: 10.1007/s10827-008-0087-5

16. Druckmann, S., Berger, T., Hill, S., Schürmann, F., Markram, H., & Segev I. 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. Calì, C., Berger, TK., Pignatelli, M., Carleton, A., Markram, H., & Giugliano M. 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. Melamed, O., Barak, O., Silberberg, G., Markram, H., & Tsodyks M. 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. Ascoli, GA., 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. Markram, H. Fixing the location and dimensions of functional neocortical columns, HFSP J. 2008 Jun;2(3):132-5.
DOI: 10.2976/1.2919545

11. Silberberg, G., & Markram, H. 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. Markram, H. Bioinformatics: industrializing neuroscience. Nature. 2007 Jan 11;445(7124):160-1.
DOI: 10.1038/445160a

9. Abid, A., Jan, A., Francioli, L., Sfyrakis, K., & Schürmann F. 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.
DOI: 10.1007/978-3-540-76843-2_6

8. Druckmann, S.,Banitt, Y., Gidon, A., Schürmann, F., Markram, H., & Segev I. 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. Toledo-Rodriguez M., & Markram H. 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. Le Bé, JV., Silberberg, G., Wang, Y., & Markram H. 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

1. Muhammad, A., J., & Markram, H.NEOBASE: Databasing the Neocortical Microcircuit, Stud Health Technol Inform. 2005;112:167-77.