Publications

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

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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.
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.
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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.