Publications

281. Tharayil, J., Zinno, C., Agnesi, F., Lloyd, B., Farcito, S., Cassara, A. M., Kuster, N., Reimann, M., Micera, S., & Neufeld, E. (2024). Simulation insights on the compound action potential in multifascicular nerves. bioRxiv. http://biorxiv.org/lookup/doi/10.1101/2024.10.16.618681

280. Petkantchin, R., Berchet, A., Peng, H., Markram, H., & Kanari, L. (2024). Generating brain-wide connectome using synthetic axonal morphologies. bioRxiv. http://biorxiv.org/lookup/doi/10.1101/2024.10.04.616605

279. Petkantchin, R., Berchet, A., Peng, H., Markram, H., & Kanari, L. (2024). Generating brain-wide connectome using synthetic axonal morphologies. bioRxiv. http://biorxiv.org/lookup/doi/10.1101/2024.10.04.616605

278. Shichkova, P., Coggan, J. S., Markram, H., & Keller, D. (2024). Brain metabolism in health and neurodegeneration: The interplay among neurons and astrocytes. Cells, 13(20), 1714. https://doi.org/10.3390/cells13201714

277. Iatropoulos, G., Gerstner, W., & Brea, J. (2024). Two-factor synaptic consolidation reconciles robust memory with pruning and homeostatic scaling. bioRxiv. https://doi.org/10.1101/2024.07.23.604787

276. Tuncel, A., Geit, W. V., Gevaert, M., Torben-Nielsen, B., Mandge, D., Kılıç, İ., Jaquier, A., Muller, E., Kanari, L., & Markram, H. (2024). BlueCelluLab: Biologically detailed neural network experimentation API. Journal of Open Source Software, 9(100), 7026. https://doi.org/10.21105/joss.07026

275. Saxena, D., Fischer, A., Dranczewski, J., Ng, W. K., Trivino, N. V., Schmid, H., Raziman, T. V., Arnaudon, A., Barahona, M., Hess, O., Moselund, K., & Sapienza, R. (2024). Designed semiconductor network random lasers. Laser & Photonics Reviews, 2400623. https://doi.org/10.1002/lpor.202400623

274. Abdellah, M., Foni, A., Cantero, J. J. G., Guerrero, N. R., Boci, E., Fleury, A., Coggan, J. S., Keller, D., Planas, J., Courcol, J.-D., & Khazen, G. (2024). Synthesis of geometrically realistic and watertight neuronal ultrastructure manifolds for in silico modeling. Briefings in Bioinformatics, 25(5), bbae393. https://doi.org/10.1093/bib/bbae393

273. Honaryar, H., Amirfattahi, S., Nguyen, D., Kim, K., Shillcock, J. C., & Niroobakhsh, Z. (2024). A versatile approach to stabilize liquid–liquid interfaces using surfactant selfassembly. Small, 2403013. https://doi.org/10.1002/smll.202403013

272. Lorin, C., Guiet, R., Chiaruttini, N., Ambrosini, G., Boci, E., Abdellah, M., Markram, H., & Keller, D. (2024). Structural and molecular characterization of astrocyte and vasculature connectivity in the mouse hippocampus and cortex. Glia, 1–21. https://doi.org/10.1002/glia.24594

271. Logette, E., Ranjan, R. (2024). Ready-to-record cells for kinetic screening of VGICs.In: Furini, S. (eds) Potassium Channels. Methods in Molecular Biology, vol 2796. Springer Protocols, Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3818-7_5

270. Bolaños-Puchet, S., Teska, A., Hernando, J. B., Lu, H., Romani, A., Schürmann, F., & Reimann, M. W. (2024). Enhancement of brain atlases with laminar coordinate systems: Flatmaps and barrel column annotations. Imaging Neuroscience, 2, 1–20. https://doi.org/10.1162/imag_a_00209

269. Nurisso, M., Arnaudon, A., Lucas, M., Peach, R. L., Expert, P., Vaccarino, F., & Petri, G. (2024). A unified framework for simplicial Kuramoto models. Chaos: An Interdisciplinary Journal of Nonlinear Science, 34(5), 053118. https://doi.org/10.1063/5.0169388

268. 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. (2024). A multimodal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays. Neural Computation, 1–46. https://doi.org/10.1162/neco_a_01672

267. Pokorny, C., Awile, O., Isbister, J. B., Kurban, K., Wolf, M., & Reimann, M. W. (2024). A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations. bioRxiv. http://biorxiv.org/lookup/doi/10.1101/2024.05.24.593860

266. Tharayil, J., Blanco Alonso, J., Farcito, S., Lloyd, B., Cassara, A., Schürmann, F., Neufeld, E., Kuster, N., & Reimann, M. (2024). BlueRecording: A pipeline for the efficient calculation of extracellular recordings in large-scale neural circuit models. bioRxiv. https://doi.org/10.1101/2024.05.14.591849

265. Santander, D. E., Pokorny, C., Ecker, A., Lazovskis, J., Santoro, M., Smith, J. P., Hess, K., Levi, R., & Reimann, M. W. (2024). Efficiency and reliability in biological neural network architectures. bioRxiv. http://biorxiv.org/lookup/doi/10.1101/2024.03.15.585196

264. Ecker, A., Egas Santander, D., Bolaños-Puchet, S., Isbister, J. B., & Reimann, M. W. (2024). Cortical cell assemblies and their underlying connectivity: An in silico study. PLOS Computational Biology, 20(3), e1011891. https://doi.org/10.1371/journal.pcbi.1011891

263. Reimann, M. W., Egas Santander, D., Ecker, A., & Muller, E. B. (2023). Specific inhibition and disinhibition in the higher-order structure of a cortical connectome. bioRxiv. https://doi.org/10.1101/2023.12.22.573036

262. Vitale, P., Librizzi, F., Vaiana, A. C., Capuana, E., Pezzoli, M., Shi, Y., Romani, A., Migliore, M., & Migliore, R. (2023). Different responses of mice and rats hippocampus CA1 pyramidal neurons to in vitro and in vivo-like inputs. Frontiers in Cellular Neuroscience, 17, 1281932. https://doi.org/10.3389/fncel.2023.1281932

261. Weise, K., Worbs, T., Kalloch, B., Souza, V. H., Jaquier, A. T., Van Geit, W., Thielscher, A., & Knösche, T. R. (2023). Directional sensitivity of cortical neurons towards TMS induced electric fields.Imaging Neuroscience. 1:1-22. https://doi.org/10.1162/imag_a_00036

260. Bologna, L. L., Tocco, A., Smiriglia, R., Romani, A., Schürmann, F., & Migliore, M. (2023). Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub. Frontiers in Neuroinformatics, 17, 1271059. https://doi.org/10.3389/fninf.2023.1271059

259. Arnaudon, A., Reva, M., Zbili, M., Markram, H., Van Geit, W., & Kanari, L. (2023). Controlling morpho-electrophysiological variability of neurons with detailed biophysical models. iScience (Cell Press), 108222. https://doi.org/10.1016/j.isci.2023.108222

258. Reva, M., Rössert, C., Arnaudon, A., Damart, T., Mandge, D., Tuncel, A., Ramaswamy, S., Markram, H., & Van Geit, W. (2023). A universal workflow for creation, validation and generalization of detailed neuronal models. Patterns (Cell Press), 100855. https://doi.org/10.1016/j.patter.2023.100855

257. Kanari, L., Shi, Y., Arnaudon, A., Barros Zulaica, N., Benavides-Piccione, R., Coggan, J. S., DeFelipe, J., Hess, K., Mansvelder, H. D., Mertens, E. J., Segev, I., Markram, H., & De Kock, C. P. J. (2023). Of mice and men: Increased dendritic complexity gives rise to unique human networks. bioRxiv.  http://biorxiv.org/lookup/doi/10.1101/2023.09.11.557170

256. Brown, A. D., Beaumont, J. R., Thomas, D. B., Shillcock, J. C., Naylor, M. F., Bragg, G. M., Vousden, M. L., Moore, S. W., & Fleming, S. T. (2023). Partially Ordered Event Triggered System (POETS): An event-driven approach to dissipative particle dynamics: Implementing a massively compute-intensive problem on a novel hard/software architecture. ACM Transactions on Parallel Computing. 10(2). https://doi.org/10.1145/3580372

255. Shichkova, P., Coggan, J. S., Boci, E., Favreau, C. P. H., Antonel, S. M., Markram, H., & Keller, D. (2023).Breakdown and rejuvenation of aging brain energy metabolism. bioRxiv.http://biorxiv.org/lookup/doi/10.1101/2023.08.30.555341

254. Gosztolai, A., Peach, R. L., Arnaudon, A., Barahona, M., & Vandergheynst, P. (2023). Interpretable statistical representations of neural population dynamics and geometry. arXiv.https://doi.org/10.48550/ARXIV.2304.03376

253. Ecker, A., Santander, D. E., Abdellah, M., Alonso, J. B., Bolaños-Puchet, S., Chindemi, G., Isbister, J. B., King, J. G., Kumbhar, P., Magkanaris, I., Muller, E. B., & Reimann, M. W. (2023). Long-term plasticity induces sparse and specific synaptic changes in a biophysically detailed cortical model. bioRxiv. 7 August 2023. http://biorxiv.org/lookup/doi/10.1101/2023.08.07.552264

252. Curry, J., DeSha, J., Garin, A., Hess, K., Kanari, L., & Mallery, B. (2023). From trees to barcodes and back again II: Combinatorial and probabilistic aspects of a topological inverse problem. Computational Geometry, version of record: 18 July 2023, Vol. 116, 103031. https://doi.org/10.1016/j.comgeo.2023.102031

251. Wei, Y., Nandi, A., Jia, X., Siegle, J. H., Denman, D., Lee, S. Y., Buchin, A., Van Geit, W., Mosher, C. P., Olsen, S., & Anastassiou, C. A. (2023). Associations between in vitro, in vivo and in silico cell classes in mouse primary visual cortex. Nature Communications, 14(1), 2344. https://doi.org/10.1038/s41467-023-37844-8

250. Manubens-Gil, L., Zhou, Z., Chen, H., Ramanathan, A., Liu, X., Liu, Y., Bria, A., Gillette, T., Ruan, Z., Yang, J., Radojević, M., Zhao, T., Cheng, L., Qu, L., Liu, S., Bouchard, K. E., Gu, L., Cai, W., Ji, S., Roysam, B., Wang, C.-W., Yu, H., Sironi, A., Iascone, D.M., Zhou, J., Bas, E., Conde-Sousa, E., Aguiar, P., Li, X., Li, Y., Nanda, S., Wang, Y., Muresan, L., Fua, P., Ye, B., He, H., Staiger, J.F., Peter, M., Cox, D.N., Simonneau, M., Oberlaender, M., Jefferis, G., Ito, K., Gonzalez-Bellido, P., Kim, J., Rubel, E., Cline, H.T., Zeng, H., Nern, A., Chiang, A.-S., Yao, J., Roskams, J., Livesey, R., Stevens, J., Liu, T., Dang, C., Guo, Y., Zhong, N., Tourassi, G., Hill, S., Hawrylycz, M., Koch, C., Meijering, E., Ascoli, G.A., & Peng, H. (2023). BigNeuron: A resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets. Nature Methods, 20(6), 824–835. https://doi.org/10.1038/s41592-023-01848-5

249. Isbister, J. B., Ecker, A., Pokorny, C., Bolaños-Puchet, S., Egas Santander, D., Arnaudon, A., Awile, O., Barros-Zulaica, N., Blanco Alonso, J., Boci, E., Chindemi, G., Courcol, J.-D., Damart, T., Delemontex, T., Dietz, A., Ficarelli, G., Gevaert, M., Herttuainen, J., Ivaska, G., Ji, W., Keller, D., King, J., Kumbhar, P., Lapere, S., Litvak, P., Mandge, D., Muller, E.B., Pereira, F., Planas, J., Ranjan, R., Reva, M., Romani, A., Rössert, C., Schürmann, F., Sood, V., Teska, A., Tuncel, A., Van Geit, W., Wolf, M., Markram, H., Ramaswamy, S., & Reimann, M. W. (2023). Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentation. bioRxiv. 17 May 2023.http://biorxiv.org/lookup/doi/10.1101/2023.05.17.541168

248. Romani, A., Antonietti, A., Bella, D., Budd, J., Giacalone, E., Kurban, K., Sáray, S., Abdellah, M., Arnaudon, A., Boci, E., Colangelo, C., Courcol, J.-D., Delemontex, T., Ecker, A., Falck, J., Favreau, C., Gevaert, M., Hernando, J. B., Herttuainen, J., Ivaska, G., Kanari, L., Kaufmann, A.-K., King, J.G., Kumbhar, P., Lange, S., Lu, H., Lupascu, C.A., Migliore, R., Petitjean, F., Planas, J., Rai, P., Ramaswamy, S., Reimann, M.W., Riquelme, J.L., Guerrero, N.R., Shi, Y., Sood, V., Sy, M.F., Geit, W.V., Vanherpe, L., Freund, T.F., Mercer, A., Muller, E., Schürmann, F., Thomson, A.M., Migliore, M., Káli, S., & Markram, H. (2023). Community-based reconstruction and simulation of a full-scale model of region CA1 of rat hippocampus. bioRxiv. 17 May 2023. http://biorxiv.org/lookup/doi/10.1101/2023.05.17.541167

247. 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

246. Guyonnet-Hencke, T., & Reimann, M. W. (2023). A parcellation scheme of mouse isocortex based on reversals in connectivity gradients. Network Neuroscience, 7(3), 999–1021.   https://doi.org/10.1162/netn_a_00312

245. Mitenkov, G., Magkanaris, I., Awile, O., Kumbhar, P., Schürmann, F., & Donaldson, A. F. (2023). MOD2IR: High-performance code generation for a biophysically detailed neuronal simulation DSL. Proceedings of the 32nd ACM SIGPLAN International Conference on Compiler Construction, 203–215. https://doi.org/10.1145/3578360.3580268

244. Aimone JB, Awile O, Diesmann M, Knight JC, Nowotny T & Schürmann F. (2023) Editorial: Neuroscience, computing, performance, and benchmarks: Why it matters to neuroscience how fast we can compute. Front. Neuroinform. Volume 17. https://www.frontiersin.org/articles/10.3389/fninf.2023.1157418/full

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

242. 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., Baayen, J. C., Lein, E. S., DeFelipe, J., Markram, H., Mansvelder, H. D., Schürmann, F., Segev, I., & de Kock, C. P. J. (2023). Strong and reliable synaptic communication between pyramidal neurons in adult human cerebral cortex. Cerebral Cortex, 33(6), 2857–2878. https://doi.org/10.1093/cercor/bhac246

241. 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

240. Shillcock, J. C., Thomas, D. B., Ipsen, J. H., & Brown, A. D. (2023). Macromolecular crowding is surprisingly unable to deform the structure of a model biomolecular condensate. Biology, 12(2), 181. https://doi.org/10.3390/biology12020181

239. 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

238. Iatropoulos, G., Brea, J., & Gerstner, W. (2022). Kernel memory Networks: A Unifying Framework for Memory Modeling. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in Neural Information Processing Systems (Vol. 35, pp. 35326–35338). Curran Associates, Inc.. Thirty-sixth Conference on Neural Information Processing Systems, 28 Nov – 9 Dec 2022, New Orleans, LA, USA. Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2022/…Paper-Conference.pdf

237. Christensen, D. V., Dittmann, R., Linares-Barranco, B., Sebastian, A., Le Gallo, M., Redaelli, A., Slesazeck, S., Mikolajick, T., Spiga, S., Menzel, S., Valov, I., Milano, G., Ricciardi, C., Liang, S.-J., Miao, F., Lanza, M., Quill, T. J., Keene, S. T., Salleo, A., Grollier, J., Marković, D., Mizrahi, A., Yao, P., Yang, J.J., Indiveri, G., Strachan, J.P., Datta, S., Vianello, E., Valentian, A., Feldmann, J., Li, X., Pernice, W.H.P., Bhaskaran, H., Furber, S., Neftci, E., Scherr, F., Maass, W., Ramaswamy, S., Tapson, J., Panda, P., Kim, Y., Tanaka, G., Thorpe, S., Bartolozzi, C., Cleland, T.A., Posch, C., Liu, S., Panuccio, G., Mahmud, M., Mazumder, A.N., Hosseini, M., Mohsenin, T., Donati, E., Tolu, S., Galeazzi, R., Christensen, M.E., Holm, S., Ielmini, D., & Pryds, N. (2022). 2022 Roadmap on neuromorphic computing and engineering. Neuromorphic Computing and Engineering, 2(2), 022501. https://doi.org/10.1088/2634-4386/ac4a83

236. 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

235. 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

234. 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

233. 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

232. 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

231. 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

230. 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

229. 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

228. 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

227. 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

226. 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

225. 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.

224. 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

223. 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

222. 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

221. 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

220. 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

219. 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

218. 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

217. 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

216. 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

215. 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

214. 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

213. 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

212. 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

211. 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

210. 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

209. 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

208. 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

207. 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

206. 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 Access9, 163861–163881. https://doi.org/10.1109/ACCESS.2021.3133078.

205. 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

204. 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

203. 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

202.  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

201. 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

200. 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.

199. 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.

198. 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

197. 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

196. 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

195. 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 

194. 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

193. 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

192. 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

191. 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

190. 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

189. 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

188. 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

187. Blundell, I., Brette, R., Cleland, T. A., Close, T. G., Coca, D., Davison, A. P., Diaz-Pier, S., Fernandez Musoles, C., Gleeson, P., Goodman, D. F. M., Hines, M., Hopkins, M. W., Kumbhar, P., Lester, D. R., Marin, B., Morrison, A., Müller, E., Nowotny, T., Peyser, A., … Eppler, J. M. (2018).Code Generation in Computational Neuroscience: A Review of Tools and TechniquesFrontiers in Neuroinformatics12, 68. https://doi.org/10.3389/fninf.2018.00068

186. Criado, J., Garcia-Gasulla, M., Kumbhar, P., Awile, O., Magkanaris, I., & Mantovani, F. (2020). CoreNEURON: Performance and energy efficiency evaluation on Intel and Arm CPUs. 2020 IEEE International Conference on Cluster Computing (CLUSTER), 540–548. https://doi.org/10.1109/CLUSTER49012.2020.00077

185. Allegra Mascaro, A. L., Falotico, E., Petkoski, S., Pasquini, M., Vannucci, L., Tort-Colet, N., Conti, E., Resta, F., Spalletti, C., Ramalingasetty, S. T., Von Arnim, A., Formento, E., Angelidis, E., Blixhavn, C. H., Leergaard, T. B., Caleo, M., Destexhe, A., Ijspeert, A., Micera, S., Laschi, C., Jirsa, V., Gewaltig, M.-O., Pavone, F. S. (2020).Experimental and computational study on motor control and recovery after stroke: Toward a constructive loop between experimental and virtual embodied neuroscience. Frontiers in Systems Neuroscience, 14, 31. https://doi.org/10.3389/fnsys.2020.00031

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

183. 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.

182. 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 silicoHippocampus, Wiley. 30(11), 1129–1145. https://doi.org/10.1002/hipo.23220.

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

180. 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 VessMorphoVisBioinformatics, Oxford University Press. Vol. 36 (Supplement_1), i534–i541. https://doi.org/10.1093/bioinformatics/btaa461.

179. Damart, T., Van Geit, W., & Markram, H. (2020). Data driven building of realistic neuron model using IBEA and CMA evolution strategiesGECCO ’20 Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 35–36. https://doi.org/10.1145/3377929.3398161

178. 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.

177. 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.

176. 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

175. 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

174. 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

173. 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 modelsPLOS Computational Biology, 16(2), e1007696.
DOI: 10.1371/journal.pcbi.1007696.

172. 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 signallingJournal of Theoretical Biology, 487, 110123.
DOI: 10.1016/j.jtbi.2019.110123.

171. 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.

170. Nolte, M., Gal, E., Markram, H., & Reimann, M.W. (2020). Impact of higher-order network structure on emergent cortical activityNetwork Neuroscience. 4(1), 292–314.
DOI: 10.1162/netn_a_00124.

169. 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

168. 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

167. Karlsson, J., Abdellah, M., Foni, A., Lapere, S., & 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.

166. Magalhães, B.R.C., Sterling, T., Schürmann Felix, & 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: 10.1109/IPDPS.2019.00028.

165. Abdellah, M., Favreau, C., Hernando, J., Lapere, S., & 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: 10.2312/cgvc.20191257.

164. Barros-Zulaica, N., Rahmon, J., Chindemi, G., Perin, R., Markram, H., Ramaswamy, S., & Muller, E. Estimating the readily-releasable vesicle pool size at layer 5 pyramidal connections in the neocortex. Front. Synaptic Neurosci., 15 October 2019.
DOI: 10.3389/fnsyn.2019.00029

163. Kumbhar, P., Hines, M., Fouriaux, J., Ovcharenko, A., King, J., Delalondre, F., & Schürmann, F. (2019). CoreNEURON : An optimized compute engine for the neuron simulator. Front. Neuroinform. 13, 63.
DOI: 10.3389/fninf.2019.00063

162. Keller, D., Meystre, J., Veettil, R.V., Burri, O., Guiet, R., Schürmann, F., & Markram, H. (2019). A derived positional mapping of inhibitory subtypes in the somatosensory cortex. Front. Neuroanat. 13, 78.
DOI: 10.3389/fnana.2019.00078

161. Reimann, M.W., Gevaert, M., Shi, Y., Lu, H., Markram, H., & Muller, E. A null model of the mouse whole-neocortex micro-connectome. Nature Communications 29 August 2019.
DOI: 10.1038/s41467-019-11630-x

160. Casalegno, F., Newton, T., Daher, R., Abdelaziz, M., Lodi-Rizzini, A., Schürmann, F., Krejci, I., & Markram, H. (2019). Caries Detection with Near-Infrared Transillumination Using Deep Learning. Journal of Dental Research. Online 26 August 2019.
DOI: 10.1177/0022034519871884.

159. Nolte M., Reimann M.W., King J., Markram H., & Muller E., Cortical reliability amid noise and chaos Nature Communications, 22 August 2019,
DOI: 10.1038/s41467-019-11633-8

158. Ranjan R, Logette E, Marani M, Herzog M, Tâche V, Scantamburlo E, Buchillier V & Markram H.  A Kinetic Map of the Homomeric Voltage-Gated Potassium Channel (Kv) Family. Front. Cell. Neurosci., 20 August 2019.
DOI: 10.3389/fncel.2019.00358

157. Magalhães, B.R.C., Sterling, T., Hines, M., & Schürmann, F. (2019). Asynchronous branch-parallel simulation of detailed neuron models. Frontiers in Neuroinformatics. 13, 54.
DOI: 10.3389/fninf.2019.00054

156. 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., & 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.
DOI: 10.1016/j.neuron.2019.05.019.

155. Wybo, W.A.M., Torben-Nielsen, B., Nevian, T., & Gewaltig, M.-O. (2019). Electrical compartmentalization in neurons. Cell reports. 26, 1759-1773.e7.
DOI: 10.1016/j.celrep.2019.01.074.

154. 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: 10.1007/978-3-030-22744-9_33.

153. 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.
DOI: 10.1371/journal.pcbi.1006753

152. 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.
DOI: 10.1016/j.neuron.2019.03.027

151. Fan X & Markram H (2019). A Brief History of Simulation Neuroscience. Front. Neuroinform. 13:32.07 May 2019-
DOI: 10.3389/fninf.2019.00032

150. Colangelo, C., Shichkova, P., Keller, D., Markram, H., & Ramaswamy, S. (2019). Cellular, Synaptic and Network Effects of Acetylcholine in the Neocortex. Frontiers in Neural Circuits. 13, 24.
DOI: 10.3389/fncir.2019.00024

149. Kanari, L., Ramaswamy, S., Shi, Y., Morand, S., Meystre, Julie., Perin, R., Abdellah, M., Wang, Y., Hess, K., & Markram., H. Objective Morphological Classification of Neocortical Pyramidal Cells. Cerebral Cortex, Volume 29, Issue 4, April 2019, Pages 1719–1735,
DOI: 10.1093/cercor/bhy339

148. Barros-Zulaica, N., Villa, A.E.P., & 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.  
DOI: 10.1523/ENEURO.0471-18.2019.

147. Muddapu V.R., Mandali A., Chakravarthy V.S., & 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.

146. O’Reilly, C., Chapotot, F., Pittau, F., Mella, N., & Picard, F. (2019). Nicotine increases sleep spindle activity. Journal of Sleep Research, 28(4), e12800. https://doi.org/10.1111/jsr.12800

145. Mihaljević, B., Larrañaga, P., Benavides-Piccione, R., Hill, S., DeFelipe, J., & Bielza, C. (2018). Towards a supervised classification of neocortical interneuron morphologies. BMC Bioinformatics, 19(1), 511. https://doi.org/10.1186/s12859-018-2470-1

144. Beche, A., De, K., Delalondre, F., Schuermann, F., Klimentov, A., & Mashinistov, R. (2018). Supercomputers, clouds and grids powered by BigPanDA for brain studiesJournal of Physics: Conference Series, 1085 (3), September2018. https://doi.org/10.1088/1742-6596/1085/3/032003.

143. 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.

142. 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

141. 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.

140. 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.

139. 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.

138. 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

137. 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.

136. 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.

135. 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.

134. 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.

133. 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.

132. 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.

131. 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.

130. 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

129. 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.

128. 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.

127. 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.

126. 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

125. 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

124. 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

123. 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

122. 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

121. 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

120. 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

119. 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

118. 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

117. 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

116. 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

115. 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

114. 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

113. 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

112. 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

111. 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.

110. 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

109. Craddock, R. C., Margulies, D. S., Bellec, P., Nichols, B. N., Alcauter, S., Barrios, F. A., Burnod, Y., Cannistraci, C. J., Cohen-Adad, J., De Leener, B., Dery, S., Downar, J., Dunlop, K., Franco, A. R., Froehlich, C. S., Gerber, A. J., Ghosh, S. S., Grabowski, T. J., Hill, S., … Xu, T. (2016). Brainhack: A collaborative workshop for the open neuroscience community. GigaScience, 5(1), 16. https://doi.org/10.1186/s13742-016-0121-x

108. 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

107. 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

106. 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

105. 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

104. 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

103. 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

102. 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.

101. 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

100. 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

99. 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

98. 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

97. 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.

96. 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

95. 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

94. 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

93. 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

92. 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.

91. Richardet, R., Chappelier, J.-C., Tripathy, S., & Hill, S. (2015). Agile text mining with Sherlok. 2015 IEEE International Conference on Big Data (Big Data), 1479–1484. https://doi.org/10.1109/BigData.2015.7363910

90. Guo, Y., Friston, K., Aldo, F., Hill, S., & Peng, H. (Eds.). (2015). Brain Informatics and Health. 8th International Conference, BIH 2015 Proceedings (Vol. 9250). Springer International Publishing. London, UK, August 30 – September 2, 2015.  https://link.springer.com/10.1007/978-3-319-23344-4

89. Hill, S. (2015). Cortical Columns, Models of. In D. Jaeger & R. Jung (Eds.), Encyclopedia of Computational Neuroscience (pp. 868–871). Springer New York. http://link.springer.com/10.1007/978-1-4614-6675-8_571

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:

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:

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. (2012). Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits. Proceedings of the National Academy of Sciences, 109(42). https://doi.org/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

36. 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. 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:

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 competitionBiological 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

5. Migliore, M., Cannia, C., Lytton, W.,W., Markram, H., & Hines M., L., Parallel network simulations with NEURON, J Comput Neurosci. 2006 Oct;21(2):119-29.
DOI: 10.1007/s10827-006-7949-5

4. Markram H. The blue brain project. Nat Rev Neurosci. 7, 153-160, 2006.
DOI: 10.1038/nrn1848

3. Wang, Y., Markram, H., Goodman, PH., Berger, TK., & Goldman-Rakic, J.Ma PS. Heterogeneity in the pyramidal network of the medial prefrontal cortex, Nat Neurosci. 2006 Apr;9(4):534-42.
DOI: 10.1038/nn1670

2. Le Bé, JV., & Markram H. Spontaneous and evoked synaptic rewiring in the neonatal neocortex, PNAS. 2006 Aug 29;103(35):13214-9.
DOI:

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