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271. 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.

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269. 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.

268. 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.

267. 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.

266. 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.

265. 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. bioRxiv, 2024.03.11.584388.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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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.

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.

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.

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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.

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.

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.

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.

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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.

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.

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.

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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.

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.

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.

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.

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.

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.

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.

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.

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.

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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).

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.

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.

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.

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.

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.

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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.

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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.

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.

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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.

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.

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206. Tata Ramalingasetty, S., Danner, S. M., Arreguit, J., Markin, S. N., Rodarie, D., Kathe, C., Courtine, G., Rybak, I. A., & Ijspeert, A. J. (2021). A whole-body musculoskeletalmodel of the mouse. IEEE Access9, 163861–163881.

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.

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.

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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.
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).

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).

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.
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.

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.

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.

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.

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