Publications

259. 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

258. 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

257. 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

256. 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

255. 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

254. 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

253. 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

252. 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

251. 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

250. 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

249. 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

248. 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

247. 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

246. 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

245. 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

244. 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

243. 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

242. 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

241. 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

240. 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

239. 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

238. 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

237. 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

236. 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

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

234. 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

233. 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

232. 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

231. 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

230. 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

229. 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

228. 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

227. 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

226. 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

225. 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

224. 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.

223. 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

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 Access, 9, 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

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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.
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79. Ramaswamy, S., & Markram H. (2015). Anatomy and Physiology of the thick-tufted layer 5 pyramidal neuron, Front Cell Neurosci. 9:233.
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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.
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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.
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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.
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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.
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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.
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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).
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59. Adaszewski, S. (2014). Mynodbcsv: lightweight zero-config database solution for handling very large C SV files. PLoS ONE. 9, e103319.
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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.]
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54. Hay, E., Schürmann, F., Markram, H., & Segev, I. Preserving axosomatic spiking features despite diverse dendritic morphology. J Neurophysiol, 109(12), 2972-2981, 2013.
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53. Hernando, J., B., Biddiscombe, J., Bohara, B., Eilemann, S., & Schürmann F. Practical parallel rendering of detailed neuron simulations, EGPGV. 2013.
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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.
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50. Markram H. Seven challenges for Neuroscience. 2013. Functional Neurology. 28(3):145-51.
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49. Kandel, ER., Markram, H., Matthews, PM., Yuste, & Koch C. Neuroscience thinks big (and collaboratively). 2013. Nat Rev Neurosci. 14(9):659-64.
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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.
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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.
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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.
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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.
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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.
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40. Tauheed, F., Heinis, Schürmann, F.,Markram, H., & SCOUT A., A. Prefetching of Latent Structure Following Queries, VLDB 2012.
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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.
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38. Eilemann, S., Bilgili, A., Abdellah, M., Hernando, J., Makhinya, M., Pajarola, R., & Schürmann F. Parallel Rendering on Hybrid Multi-GPU Clusters, EGPGV 2012.
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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.
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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.
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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.
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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.
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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.
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31. Perin, R., Berger, T., K., & Markram H. A synaptic organizing principle for cortical neuronal groups, PNAS, 2011, 108 (12).
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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.
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29. Anastassiou, CA., Perin, R., Markram, H., & Koch C. Ephaptic coupling of cortical neurons, Nat Neurosci. 2011 Feb;14(2):217-23.
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25. Bar-Ilan, L., Gidon, A., & Segev I. Inter-regional synaptic competition in neurons with multiple STDP-inducing signals, J Neurophysiol (December 1, 2010).
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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.
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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.
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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.