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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.

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213. 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.

212. 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.

211. 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.

210. 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.

209. 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.

208. 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.

207. 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.

206. Shillcock, J. C., Thomas, D. B., Beaumont, J. R., Bragg, G. M., Vousden, M. L., & Brown, A. D. (2021).Coupling bulk phase separation of disordered proteins to membrane domain formation in molecular simulations on a bespoke compute fabric. Membranes, 12(1), 17.

205. 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.

204. 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. (2021). Microglial MorphOMICs unravel region- and sex-dependent morphological phenotypes from postnatal development to degeneration. bioRxiv, 1 December 2021.

203. 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.

202. Appukuttan, S., Bologna, L. L., Migliore, M., Schürmann, F., & Davison, A. P. (2021). EBRAINS Live Papers—Interactive resource sheets for computational studies in neuroscience. OSF Preprint. 2 October 2021.

201. Roussel, Y., Verasztó, C., Rodarie, D., Damart, T., Reimann, M. W., Ramaswamy, S., Markram, H., & Keller, D. (2021). Mapping of morpho-electric features to molecular identity of cortical inhibitory neurons. bioRxiv, 25 November 2021.

200. Rodarie, D., Veraszto, C., Roussel, Y., Reimann, M., Keller, D., Ramaswamy, S., Gewaltig, M.-O., & Markram, H. (2021). Atlas of inhibitory neurons in the mouse brain. bioRxiv. 22 November 2021.

199. 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.

198. Simko, J., & Markram, H. (2021). Morphology, physiology and synaptic connectivity of local interneurons in the mouse somatosensory thalamus. The Journal of Physiology, 599(22), 5085–5101.

197.  Gal, E., Amsalem, O., Schindel, A., London, M., Schürmann, F., Markram, H., & Segev, I. (2021). The role of hub neurons in modulating cortical dynamics. Frontiers in Neural Circuits, 15, 96.

196. Zisis, E., Keller, D., Kanari, L., Arnaudon, A., Gevaert, M., Delemontex, T., Coste, B., Foni, A., Abdellah, M., Calì, C., Hess, K., Magistretti, P. J., Schürmann, F., & Markram, H. (2021). Digital reconstruction of the neuro-glia-vascular architecture. Cerebral Cortex, 31(12), 5686–5703.

195. Pezeshkian, W., Shillcock, J. C., & Ipsen, J. H. (2021). Computational approaches to explore bacterial toxin entry into the host cell. Toxins, 13(7), 449.

194. Gosztolai, A., & Arnaudon, A. (2021). Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature. Nature Communications, 12(1), 4561.

193. Isbister, J. B., Reyes-Puerta, V., Sun, J.-J., Horenko, I., & Luhmann, H. J. (2021). Clustering and control for adaptation uncovers time-warped spike time patterns in cortical networks in vivo. Scientific Reports, 11(1), 15066.

192. Logette, E., Lorin, C., Favreau, C., Oshurko, E., Coggan, J. S., Casalegno, F., Sy, M. F., Monney, C., Bertschy, M., Delattre, E., Fonta, P.-A., Krepl, J., Schmidt, S., Keller, D., Kerrien, S., Scantamburlo, E., Kaufmann, A.-K., & Markram, H. (2021). A machine-generated view of the role of blood glucose levels in the severity of COVID-19. Frontiers in Public Health, 9, 1068.

191. Krepl, J., Casalegno, F., Delattre, E., Erö, C., Lu, H., Keller, D., Rodarie, D., Markram, H., & Schürmann, F. (2021). Supervised learning with perceptual similarity for multimodal gene expression registration of a mouse brain atlas. Frontiers in Neuroinformatics, 15, 37.

190. Abdellah, M., Foni, A., Zisis, E., Guerrero, N. R., Lapere, S., Coggan, J. S., Keller, D., Markram, H., & Schürmann, F. (2021). Metaball skinning of synthetic astroglial morphologies into realistic mesh models for visual analytics and in silico simulations. Bioinformatics, 37(Supplement_1), i426–i433. 

189. Peach, R. L., Arnaudon, A., Schmidt, J. A., Palasciano, H. A., Bernier, N. R., Jelfs, K. E., Yaliraki, S. N., & Barahona, M. (2021). HCGA: Highly comparative graph analysis for network phenotyping. Patterns, 2(4), 100227, Cell Press.

188. Newton, T. H., Reimann, M. W., Abdellah, M., Chevtchenko, G., Muller, E. B., & Markram, H. (2021). In silico voltage-sensitive dye imaging reveals the emergent dynamics of cortical populations.Nature Communications, 12(1), 3630.

187. O’Reilly, C., Iavarone, E., Yi, J., & Hill, S. L. (2021). Rodent somatosensory thalamocortical circuitry: Neurons, synapses, and connectivity. Neuroscience & Biobehavioral Reviews, 126, 213–235.

186. Petersen, C. C. H., Knott, G. W., Holtmaat, A., & Schürmann, F. (2021). Toward biophysical mechanisms of neocortical computation after 50 years of barrel cortex research. Function, 2(1) zqaa046. Oxford Univ. Press for the American Physiological Society.

185. Courcol, J.-D., Invernizzi, C. F., Landry, Z. C., Minisini, M., Baumgartner, D. A., Bonhoeffer, S., Chabriw, B., Clerc, E. E., Daniels, M., Getta, P., Girod, M., Kazala, K., Markram, H., Pasqualini, A., Martínez-Pérez, C., Peaudecerf, F. J., Peaudecerf, M. S., Pfreundt, U., Roller, B. R. K., Słomka, J., Vasse, M., Wheeler, J.D., Metzger, C.M.J.A., Stocker, R., and Schürmann, F. (2021). ARC: An open web-platform for request/supply matching for a prioritized and controlled COVID-19 response. Frontiers in Public Health, 9, 71.

184. Sáray, S., Rössert, C. A., Appukuttan, S., Migliore, R., Vitale, P., Lupascu, C. A., Bologna, L. L., Van Geit, W., Romani, A., Davison, A. P., Muller, E., Freund, T. F., & Káli, S. (2021). HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data. PLOS Computational Biology, 17(1), e1008114.

183. Magalhaes, B., & Schürmann, F. (2020). Efficient distributed transposition of large-scale multigraphs and high-cardinality sparse matricesarXiv, 10December2020.

182. Kanari, L., Garin, A., & Hess, K. (2020). From trees to barcodes and back again: Theoretical and statistical perspectives. Algorithms, 2020, 13(12), 335 (Special issue: Topological Data Analysis).

181. Ecker, A., Romani, A., Sáray, S., Káli, S., Migliore, M., Falck, J., Lange, S., Mercer, A., Thomson, A. M., Muller, E., Reimann, M. W., & Ramaswamy, S. (2020). Data‐driven integration of hippocampal CA1 synaptic physiology in silicoHippocampus, Wiley. 30(11), 1129–1145.

180. Ewart, T., Cremonesi, F., Schürmann, F., & Delalondre, F. (2020). Polynomial evaluation on superscalar architecture, applied to the elementary function exACM Transactions on Mathematical Software, 46(3). Association for Computing Machinery.

179. Abdellah, M., Guerrero, N. R., Lapere, S., Coggan, J. S., Keller, D., Coste, B., Dagar, S., Courcol, J.-D., Markram, H., & Schürmann, F. (2020). Interactive visualization and analysis of morphological skeletons of brain vasculature networks with VessMorphoVisBioinformatics, Oxford University Press. Vol. 36 (Supplement_1), i534–i541.

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

177. Magalhães, B., Hines, M. L., Sterling, T., & Schürmann, F. (2020). Fully-asynchronous fully-implicit variable-order variable-timestep simulation of neural networks. In Krzhizhanovskaya, V. et al. (Eds.),ICCS 2020 Amsterdam: Lecture Notes in Computer Science, vol 12141. Springer, Cham.

176. Kumbhar, P., Awile, O., Keegan, L., Blanco Alonso, J., King, J., Hines, M., & Schürmann, F. (2020). An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language. In Krzhizhanovskaya, V. et al. (Eds.), ICCS 2020 Amsterdam: Lecture Notes in Computer Science, vol 12137. Springer, Cham.

175. Amsalem, O., King, J., Reimann, M., Ramaswamy, S., Muller, E.,  Markram, Nelken, H & Segev, I. (2020). Dense computer replica of cortical microcircuits unravels cellular underpinnings of auditory surprise response. bioRxiv, 2020.05.31.
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174. Gal, E., Perin, R., Markram, H., London, M., & Segev, I. (2020). Neuron geometry underlies universal network features in cortical microcircuits. bioRxiv, 2020.05.07.

173. Chindemi, G., Abdellah, M., Amsalem, O., Benavides-Piccione, R., Delattre, V., Doron, M., Ecker, A., King, J., Kumbhar, P., Monney, C., Perin, R., Rössert, C., Van Geit, W., DeFelipe, J., Graupner, M., Segev, I., Markram, H., & Muller, E. (2020). A calcium-based plasticity model predicts long-term potentiation and depression in the neocortexbioRxiv, 2020.04.19.

172. Nandi, A., Chartrand, T., Geit, W. V., 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. (2020). Single-neuron models linking electrophysiology, morphology and transcriptomics across cortical cell typesbioRxiv.

171. Cremonesi, F., Hager, G., Wellein, G., and Schürmann, F. Analytic performance modeling and analysis of detailed neuron simulations April 3, 2020, The International Journal of High Performance Computing Applications. 34(4), 428–449. SAGE Publishing.
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170. Dai, K., Hernando, J., Billeh, Y.N., Gratiy, S.L., Planas, J., Davison, A.P., Dura-Bernal, S., Gleeson, P., Devresse, A., Dichter, B.K., Gevaert, M., King, J.G., Van Geit, W.A.H., Povolotsky, A.V., Muller, E., Courcol, J.-D., and Arkhipov, A. (2020). The SONATA data format for efficient description of large-scale network modelsPLOS Computational Biology, 16(2), e1007696.
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169. Coggan, J.S., Keller, D., Markram, H., Schürmann, F., and Magistretti, P.J. (2020). Excitation states of metabolic networks predict dose-response fingerprinting and ligand pulse phase signallingJournal of Theoretical Biology, 487, 110123.
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168. Cremonesi, F., and Schürmann, F. (2020). Understanding computational costs of cellular-level brain tissue simulations through analytical performance models. Neuroinformatics. 18, 407–428.
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167. Nolte, M., Gal, E., Markram, H., and Reimann, M.W. (2020). Impact of higher-order network structure on emergent cortical activityNetwork Neuroscience. 4(1), 292–314.
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166. Bryson, A., Hatch, R.J., Zandt, B.-J., Rossert, C., Berkovic, S.F., Reid, C.A., Grayden, D.B., Hill, S.L., and Petrou, S. (2020). GABA-mediated tonic inhibition differentially modulates gain in functional subtypes of cortical interneurons. Proceedings of the National Academy of Sciences, 117(6), 3192-3202.  DOI: 10.1073/pnas.1906369117

165. Amsalem, O., Eyal, G., Rogozinski, N., Gevaert, M., Kumbhar,P., Schürmann, F., Segev, I. An efficient analytical reduction of detailed nonlinear neuron models. Nat Commun 11, 288
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164. Karlsson, J., Abdellah, M., Foni, A., Lapere, S., and Schürmann, F. (2019). High fidelity visualization of large scale digitally reconstructed brain circuitry with signed distance functions. In 2019 IEEE Visualization Conference (VIS), 20-25 Oct. 2019, 176–180.
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163. Magalhães, B.R.C., Sterling, T., Schürmann Felix, and Hines, M.L. (2019). Exploiting flow graph of system of odes to accelerate the simulation of biologically-detailed neural networks. In the proceeding of IEEE 2019 International Parallel and Distributed Processing Symposium (IPDPS), (Rio de Janeiro, Brazil), 176–187.
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162. Abdellah, M., Favreau, C., Hernando, J., Lapere, S., and Schürmann, F. (2019). Generating high fidelity surface meshes of neocortical neurons using skin modifiers. In Eurographics proceedings UK Computer Graphics & Visual Computing, F. Vidal, G. Tam, and J. Roberts, Eds. (Bangor University, Wales, UK: The Eurographics Association), 45–53. 
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161. Barros-Zulaica, N., Rahmon, J., Chindemi, G., Perin, R., Markram, H., Ramaswamy, S., and Muller, E. Estimating the readily-releasable vesicle pool size at layer 5 pyramidal connections in the neocortex. Front. Synaptic Neurosci., 15 October 2019.
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160. Kumbhar, P., Hines, M., Fouriaux, J., Ovcharenko, A., King, J., Delalondre, F., and Schürmann, F. (2019). CoreNEURON : An optimized compute engine for the neuron simulator. Front. Neuroinform. 13, 63.
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159. Keller, D., Meystre, J., Veettil, R.V., Burri, O., Guiet, R., Schürmann, F., and Markram, H. (2019). A derived positional mapping of inhibitory subtypes in the somatosensory cortex. Front. Neuroanat. 13, 78.
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158. Reimann, M.W., Gevaert, M., Shi, Y., Lu, H., Markram, H., and Muller, E. A null model of the mouse whole-neocortex micro-connectome. Nature Communications 29 August 2019.
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157. Casalegno, F., Newton, T., Daher, R., Abdelaziz, M., Lodi-Rizzini, A., Schürmann, F., Krejci, I., and Markram, H. (2019). Caries Detection with Near-Infrared Transillumination Using Deep Learning. Journal of Dental Research. Online 26 August 2019.
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156. Nolte M., Reimann M.W., King J., Markram H., Muller E., Cortical reliability amid noise and chaos Nature Communications, 22 August 2019,
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155. Ranjan R, Logette E, Marani M, Herzog M, Tâche V, Scantamburlo E, Buchillier V and Markram H.  A Kinetic Map of the Homomeric Voltage-Gated Potassium Channel (Kv) Family. Front. Cell. Neurosci., 20 August 2019.
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154. Magalhães, B.R.C., Sterling, T., Hines, M., and Schürmann, F. (2019). Asynchronous branch-parallel simulation of detailed neuron models. Frontiers in Neuroinformatics 13, 54.
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153. Gleeson, P., Cantarelli, M., Marin, B., Quintana, A., Earnshaw, M., Sadeh, S., Piasini, E., Birgiolas, J., Cannon, R.C., Cayco-Gajic, N.A., Crook, S., Davison, A.P., Dura-Bernal, S., Ecker, A., Hines, M.L., Idili, G., Lanore, F., Larson, S.D., Lytton, W.W., Majumdar, A., McDougal, R.A., Sivagnanam, S., Solinas, S., Stanislovas, R., van Albada, S.J., van Geit, W., and Silver, R.A. (2019). Open source brain: a collaborative resource for visualizing, analyzing, simulating, and developing standardized models of neurons and circuits. Neuron. Online 11 June 2019.
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152. Wybo, W.A.M., Torben-Nielsen, B., Nevian, T., and Gewaltig, M.-O. (2019). Electrical compartmentalization in neurons. Cell reports 26, 1759-1773.e7.
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151. Magalhães B.R.C., Sterling T., Hines M., Schürmann F. (2019) Fully-Asynchronous Cache-Efficient Simulation of Detailed Neural Networks. In: Rodrigues J. et al. (eds) Computational Science – ICCS 2019. ICCS 2019. Lecture Notes in Computer Science, vol 11538. Springer International Publishing, 421–434.
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150. Iavarone E., Yi J., Shi Y., Zandt B.J., O’Reilly C., Van Geit W., Rössert C., Markram, H., Hill, S.L. (2019) Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons. PLOS Computational Biology 15(5): 1-23. e1006753.
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149. Einevoll, G.T., Destexhe, A., Diesmann, M., Grün, S., Jirsa, V., Kamps, M. de, Migliore, M., Ness, T.V., Plesser, H.E., Schürmann, F. (2019). The Scientific Case for Brain Simulations. Neuron 102, 735–744.
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148. Fan X and Markram H (2019). A Brief History of Simulation Neuroscience. Front. Neuroinform. 13:32.07 May 2019-
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147. Colangelo, C., Shichkova, P., Keller, D., Markram, H., and Ramaswamy, S. (2019). Cellular, Synaptic and Network Effects of Acetylcholine in the Neocortex. Frontiers in Neural Circuits 13, 24.
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146. Kanari, L., Ramaswamy, S., Shi, Y., Morand, S., Meystre, Julie., Perin, R., Abdellah, M., Wang, Y., Hess, K., Markram., Objective Morphological Classification of Neocortical Pyramidal Cells. Cerebral Cortex, Volume 29, Issue 4, April 2019, Pages 1719–1735,
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141. Planas, J., Delalondre, F., and Schürmann, F. (2018). Accelerating Data Analysis in Simulation Neuroscience with Big Data Technologies. In Computational Science – ICCS 2018, Y. Shi, et al., eds. (Springer International Publishing), Lecture Notes in Computer Science book series (LNCS, volume 10860), 363–377.

140. Abdellah, M., Hernando, J., Eilemann, S., Lapere, S., Antille, N., Markram, H., and Schürmann, F. (2018). NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks. Bioinformatics 34, i574–i582.
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139. Coggan, J.S., Calì, C., Keller, D., Agus, M., Boges, D., Abdellah, M., Kare, K., Lehväslaiho, H., Eilemann, S., Jolivet, R.B., Hadwiger, M., Markram, H., Schürmann, F., Magistretti, P.J. (2018a). A Process for Digitizing and Simulating Biologically Realistic Oligocellular Networks Demonstrated for the Neuro-Glio-Vascular Ensemble. Frontiers in Neuroscience 12, 664.
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138. Coggan, J.S., Keller, D., Calì, C., Lehväslaiho, H., Markram, H., Schürmann, F., and Magistretti, P.J. (2018b). Norepinephrine stimulates glycogenolysis in astrocytes to fuel neurons with lactate. PLOS Computational Biology 14(8).
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136. Eyal, G., Verhoog, M.B., Testa-Silva, G., Deitcher, Y., Benavides-Piccione, R., DeFelipe, J., de Kock, C.P.J., Mansvelder, H.D., and Segev, I. (2018). Human Cortical Pyramidal Neurons: From Spines to Spikes via Models. Frontiers in Cellular Neuroscience 12, 181.
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60. J.DeFelipe, E.Garrido, H.Markram: 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., and 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., and 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., and 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. M.W. Reimann, C.A.Anastassiou, R.Perin, S.L.Hill, H. Markram, C. Koch: 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. E. Hay, F. Schürmann, H. Markram, I. Segev: Preserving axosomatic spiking features despite diverse dendritic morphology. J Neurophysiol, 109(12), 2972-2981, 2013.
DOI: 10.1152/jn.00048.2013

53. J.B. Hernando, J. Biddiscombe, B. Bohara, S. Eilemann, F. Schürmann: Practical parallel rendering of detailed neuron simulations, EGPGV 2013.
DOI: 10.2312/EGPGV/EGPGV13/049-056

52. R.Perin, M.Telefont, H.Markram: 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. A.Loebel, JV.LeBe, MJ.Richardson, H.Markram, A.Herz: Matched pre- and post-synaptic changes underlie synaptic plasticity over long time scales. 2013. J Neurosci. 33(15):6257-66.

50. H.Markram: Seven challenges for Neuroscience. 2013. Functional Neurology. 28(3):145-51.
DOI: 10.11138/FNeur/2013.28.3.145

49. ER.Kandel, H.Markram, PM.Matthews, R.Yuste, C.Koch: 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., and 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. S.Druckmann, S.Hill, F.Schürmann, H.Markram, I.Segev : 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., and 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., and 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., and 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. S.L.Hill, Y.Wang, I.Riachi, F.Schürmann, H.Markram: Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits, PNAS, Published online before print September 18, 2012.
DOI: 10.1073/pnas.1202128109

41. A.Gidon and I.Segev: 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. F.Tauheed, T.Heinis, F.Schürmann, H.Markram, A.Ailamaki: SCOUT: Prefetching of Latent Structure Following Queries, VLDB 2012.
DOI: 10.14778/2350229.2350267

39. G.Khazen, S.L.Hill, F.Schürmann , and H.Markram: 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. S.Eilemann, A.Bilgili, M.Abdellah, J.Hernando, M.Makhinya, R.Pajarola, and F.Schürmann: Parallel Rendering on Hybrid Multi-GPU Clusters, EGPGV 2012.
DOI: 10.2312/EGPGV/EGPGV12/109-117

37. S.Lasserre, J.Hernando, S.Hill, F.Schürmann, P. de Miguel Anasagasti, G.Abou Jaoudé, H.Markram : 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. R.Ranjan, G.Khazen, L.Gambazzi, S.Ramaswamy, S.L.Hill, F.Schürmann, and H.Markram: Channelpedia: an integrative and interactive database for ion channels, Front. Neuroinform 2011. 5:36.
DOI: 10.3389/fninf.2011.00036

34. M.Hines, S.Kumar, and F.Schürmann: 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. E.Hay, S.L.Hill, F.Schürmann, H.Markram, and I.Segev: 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. S.Druckmann, T.K.Berger, F.Schürmann, S.L.Hill, H.Markram, and I.Segev: Effective stimuli for constructing reliable neuron models, Plos Computational Biology, 2011, 7(8): e1002133.
DOI: 10.1371/journal.pcbi.1002133

31. R.Perin, T.K.Berger, and H.Markram: A synaptic organizing principle for cortical neuronal groups, PNAS, 2011, 108 (12).

30. S.Romand, Y.Wang, M.Toledo-Rodriguez, and H.Markram: 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. CA.Anastassiou, R.Perin, H.Markram, and C.Koch: Ephaptic coupling of cortical neurons, Nat Neurosci. 2011 Feb;14(2):217-23.
DOI: 10.1038/nn.2727

28. H. Markram, W. Gerstner, PJ. Sjöström: A history of spike-timing-dependent plasticity. Front Synaptic Neurosci. 2011;3:4. Epub 2011 Aug 29.
DOI: 10.3389/fnsyn.2011.00004

27. H. Markram, R. Perin: Innate neural assemblies for lego memory. Front Neural Circuits. 2011;5:6. Epub 2011 May 16.
DOI: 10.3389/fncir.2011.00006

26. TK.Berger, G.Silberberg, R.Perin, and H.Markram: Brief bursts self-inhibit and correlate the pyramidal network, PLoS Biol. 2010 Sep 7;8(9).
DOI: 10.1371/journal.pbio.1000473

25. L.Bar-Ilan, A.Gidon, and I.Segev: Inter-regional synaptic competition in neurons with multiple STDP-inducing signals, J Neurophysiol (December 1, 2010).
DOI: 10.1152/jn.00612.2010.

24. A.Loebel, G.Silberberg, D.Helbig, H.Markram, M.Tsodyks, MJ.Richardson: 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. TK.Berger, R.Perin, G.Silberberg, and H.Markram: 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. J.G.King, M.Hines, S.Hill, P.H.Goodman, H.Markram, F.Schürmann: 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. J.Kozloski, K.Sfyrakis, S.Hill, F.Schürmann, C.Peck, H.Markram: Identifying, tabulating, and analyzing contacts between branched neuron morphologies, IBM Journal of Research and Development, Vol 52, Number 1/2, 2008.

18. M.Hines, H.Eichner, F.Schürmann: 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. M.Hines, H.Markram, F.Schürmann: Fully Implicit Parallel Simulation of Single Neurons, J. Comput. Neurosci., 25(3):439-48, 2008.
DOI: 10.1007/s10827-008-0087-5

16. S.Druckmann, T.Berger, S.Hill, F.Schürmann, H.Markram, I.Segev: 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. C.Calì, TK.Berger, M.Pignatelli, A.Carleton, H.Markram, M.Giugliano: 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. O.Melamed, O.Barak, G.Silberberg, H.Markram, M.Tsodyks: 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. GA.Ascoli, 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. H.Markram: Fixing the location and dimensions of functional neocortical columns, HFSP J. 2008 Jun;2(3):132-5.
DOI: 10.2976/1.2919545

11. G.Silberberg and H.Markram: 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. H.Markram: Bioinformatics: industrializing neuroscience. Nature. 2007 Jan 11;445(7124):160-1.
DOI: 10.1038/445160a

9. A.Abid, A.Jan, L.Francioli, K.Sfyrakis, and F.Schürmann: 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. S.Druckmann, Y.Banitt, A.Gidon, F.Schürmann, H.Markram, and I.Segev: 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. M.Toledo-Rodriguez and H.Markram: 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. JV.Le Bé, G.Silberberg, Y.Wang, and H.Markram: 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. M.Migliore, C.Cannia, W.W.Lytton, H.Markram, and M.L.Hines: Parallel network simulations with NEURON, J Comput Neurosci. 2006 Oct;21(2):119-29.
DOI: 10.1007/s10827-006-7949-5

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

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

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

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