Publications

267. 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

266. 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

265. 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

264. 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

263. 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

262. 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

261. 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

260. 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

259. 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

258. Bolanos-Puchet, S., Teska, A., & Reimann, M. W. (2023). Enhancement of brain atlases with region-specific coordinate systems: Flatmaps and barrel column annotations. bioRxiv, 24 August 2023. https://doi.org/10.1101/2023.08.24.554204

257. Nurisso, M., Arnaudon, A., Lucas, M., Peach, R. L., Expert, P., Vaccarino, F., & Petri, G. (2023). A unified framework for Simplicial Kuramoto models. arXiv. http://arxiv.org/abs/2305.17977.

256. 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

255. 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

254. 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

253. 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

252. 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

251. 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

250. 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

249. 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

248. 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

247. 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

246. 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

245. 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

244. 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

243. 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

242. Ecker, A., Santander, D. E., Bolaños-Puchet, S., Isbister, J. B., & Reimann, M. W. (2023). Cortical cell assemblies and their underlying connectivity: An in silico study. bioRxiv, 2023.02.24. https://doi.org/10.1101/2023.02.24.529863

241. 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

240. 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

239. 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

 

238. 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

237. 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

236. 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

235. 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

234. 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

233. 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

232. 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

231. 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

230. 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

229. 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

228. 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

227. 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

226. 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.

225. 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

224. 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

223. 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

222. Buccino, A. P., Damart, T., Bartram, J., Mandge, D., Xue, X., Zbili, M., Gänswein, T., Jaquier, A., Emmenegger, V., Markram, H., Hierlemann, A., & Van Geit, W. (2022). A multi-modal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays. bioRxiv. 5 August 2022. https://doi.org/10.1101/2022.08.03.502468

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

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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

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.