Publications

192. 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. https://doi.org/10.1038/s41467-021-23901-7

191. 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. https://doi.org/10.1016/j.neubiorev.2021.03.015

190. 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. https://doi.org/10.1093/function/zqaa046

189. 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. https://doi.org/10.3389/fpubh.2021.607677

188. 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. https://doi.org/10.1371/journal.pcbi.1008114

187. 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). Architecture of the neuro-glia-vascular system. bioRxiv, 2021.01.19. https://doi.org/10.1101/2021.01.19.427241

186. Gillespie, T. H., Tripathy, S., Sy, M. F., Martone, M. E., & Hill, S. L. (2020). The Neuron Phenotype Ontology: A FAIR approach to proposing and classifying neuronal types. bioRxiv, 2020.09.02.http://biorxiv.org/lookup/doi/10.1101/2020.09.01.278879.

185. Kanari, L., Dictus, H., Chalimourda, A., Van Geit, W., Coste, B., Shillcock, J., Hess, K., & Markram, H. (2020).Computational synthesis of cortical dendritic morphologies. bioRxiv, 2020.04.17. https://doi.org/10.1101/2020.04.15.040410.

184. Reimann, M. W., Riihimäki, H., Smith, J. P., Lazovskis, J., & Levi, R. (n.d.). Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategieshttps://www.biorxiv.org/content/10.1101/2020.11.02.363929v1

183. Magalhaes, B., & Schürmann, F. (2020). Efficient distributed transposition of large-scale multigraphs and high-cardinality sparse matricesarXiv, 10December2020. http://arxiv.org/abs/2012.06012.

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). https://doi.org/10.3390/a13120335.

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. https://doi.org/10.1002/hipo.23220.

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. https://doi.org/10.1145/3408893.

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. https://doi.org/10.1093/bioinformatics/btaa461.

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. https://doi.org/10.1145/3377929.3398161

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. https://link.springer.com/chapter/10.1007%2F978-3-030-50426-7_8.

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. https://doi.org/10.1007/978-3-030-50371-0_4.

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.
DOI: 10.1101/2020.05.31.126466

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.
DOI: https://doi.org/10.1101/656058

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. https://doi.org/10.1101/2020.04.19.043117

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. https://doi.org/10.1101/2020.04.09.030239.

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.
DOI: 10.1177/1094342020912528

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.
DOI: 10.1371/journal.pcbi.1007696.

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.
DOI: 10.1016/j.jtbi.2019.110123.

168. Cremonesi, F., and Schürmann, F. (2020). Understanding computational costs of cellular-level brain tissue simulations through analytical performance models. Neuroinformatics. 18, 407–428.
DOI: 10.1007/s12021-019-09451-w.

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.
DOI: 10.1162/netn_a_00124.

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
DOI: 10.1038/s41467-019-13932-6

 

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.
DOI: 10.1109/VISUAL.2019.8933693.

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.
DOI: 10.1109/IPDPS.2019.00028.

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. 
DOI: 10.2312/cgvc.20191257.

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.
DOI: 10.3389/fnsyn.2019.00029

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.
DOI: 10.3389/fninf.2019.00063

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.
DOI: 10.3389/fnana.2019.00078

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.
DOI: 10.1038/s41467-019-11630-x

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.
DOI: 10.1177/0022034519871884.

156. Nolte M., Reimann M.W., King J., Markram H., Muller E., Cortical reliability amid noise and chaos Nature Communications, 22 August 2019,
DOI: 10.1038/s41467-019-11633-8

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.
DOI: 10.3389/fncel.2019.00358

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.
DOI: 10.3389/fninf.2019.00054

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.
DOI: 10.1016/j.neuron.2019.05.019.

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.
DOI: 10.1016/j.celrep.2019.01.074.

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.
DOI: 10.1007/978-3-030-22744-9_33.

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.
DOI: 10.1371/journal.pcbi.1006753

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.
DOI: 10.1016/j.neuron.2019.03.027

148. Fan X and Markram H (2019). A Brief History of Simulation Neuroscience. Front. Neuroinform. 13:32.07 May 2019-
DOI: 10.3389/fninf.2019.00032

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.
DOI: 10.3389/fncir.2019.00024

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,
DOI: 10.1093/cercor/bhy339

145. Barros-Zulaica, N., Villa, A.E.P., and Nuñez, A. (2019). Response adaptation in barrel cortical neurons facilitates stimulus detection during rhythmic whisker stimulation in anesthetized mice. eNeuro 6: 2. ENEURO.0471-18.2019. 25 March 2019.  
DOI: 10.1523/ENEURO.0471-18.2019.

144. Muddapu V.R., Mandali A., Chakravarthy V.S., and Ramaswamy S. (2019). A Computational Model of Loss of Dopaminergic Cells in Parkinson’s Disease Due to Glutamate-Induced Excitotoxicity. Front. Neural Circuits 13:11.
DOI: 10.3389/fncir.2019.00011.

143. Beche, A., De, K., Delalondre, F., Schuermann, F., Klimentov, A., & Mashinistov, R. (2018). Supercomputers, clouds and grids powered by BigPanDA for brain studiesJournal of Physics: Conference Series, 1085 (3), September2018. https://doi.org/10.1088/1742-6596/1085/3/032003.

142. Tieck, J. C. V., Pogančić, M. V., Kaiser, J., Roennau, A., Gewaltig, M.-O., & Dillmann, R. (2018). Learning continuous muscle control for a multi-joint arm by extending proximal policy optimization with a liquid state machine. In V. Kůrková, Y. Manolopoulos, B. Hammer, L. Iliadis, & I. Maglogiannis (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2018 (pp. 211–221). Springer International Publishing. https://doi.org/10.1007/978-3-030-01418-6_21.

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. https://www.springerprofessional.de/en/accelerating-data-analysis-in-simulation-neuroscience-with-big-d/15836908

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.
DOI: 10.1093/bioinformatics/bty231.

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.
DOI: 10.3389/fnins.2018.00664.

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).
DOI: 10.1371/journal.pcbi.1006392.

137. Erö, C., Gewaltig, M.-O., Keller, D., and Markram, H. (2018). A Cell Atlas for the Mouse Brain. Frontiers in Neuroinformatics 12, 84.
DOI: 10.3389/fninf.2018.00084

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.
DOI: 10.3389/fncel.2018.00181.

135. Johannes, L., Pezeshkian, W., Ipsen, J.H., and Shillcock, J.C. (2018). Clustering on Membranes: Fluctuations and More. Trends in Cell Biology 28, 405–415.
https://www.ncbi.nlm.nih.gov/pubmed/29502867.

134. Kanari, L., Dłotko, P., Scolamiero, M., Levi, R., Shillcock, J., Hess, K., and Markram, H. (2018a). A Topological Representation of Branching Neuronal Morphologies. Neuroinformatics 16, 3–13.
https://link.springer.com/article/10.1007%2Fs12021-017-9341-1.

133. Keller, D., Erö, C., and Markram, H. (2018). Cell Densities in the Mouse Brain: A Systematic Review. Front. Neuroanat. 12, 83.
DOI: 10.3389/fnana.2018.00083.

132. Kupper, R., Schmuker, M., Aertsen, A., Wachtler, T., Körner, U., and Gewaltig, M.-O. (2018). How the visual system can detect feature homogeneity from spike latencies. arXiv.
https://arxiv.org/abs/1806.03881.

131. Lindroos, R., Dorst, M.C., Du, K., Filipović, M., Keller, D., Ketzef, M., Kozlov, A.K., Kumar, A., Lindahl, M., Nair, A.G., Pérez-Fernández, J., Grillner, S., Silberberg, G., Hellgren Kotaleski, J. (2018). Basal ganglia neuromodulation over multiple temporal and structural scales-simulations of direct pathway MSNs investigate the fast onset of dopaminergic effects and predict the role of Kv4.2. Front Neural Circuits 12, 3.
DOI: 10.3389/fncir.2018.00003.

130. Migliore, R., Lupascu, C.A., Bologna, L.L., Romani, A., Courcol, J.-D., Antonel, S., Van Geit, W.A.H., Thomson, A.M., Mercer, A., Lange, S., Falck, J., Roessert, C. A., Freund, T. F., Kali, S., Muller, E. B., Schürmann, F., Markram, H., Migliore, M. (2018). The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow. PLOS Computational Biology 14(9).
DOI: 10.1371/journal.pcbi.1006423.

129. O’Reilly C., Chapotot F., Pittau F., Mella N., Picard F. (2018, accepted for publication). Nicotine increases sleep spindle activity. Journal of Sleep Research.
DOI: 10.1111/jsr.12800

128. Pezeshkian, W., Gao, H., Arumugam, S., Becken, U., Bassereau, P., Florent, J.-C., Ipsen, J.H., Johannes, L., and Shillcock, J.C. (2018). Mechanism of Shiga Toxin Clustering on Membranes. ACS Nano 12, 2079–2079.
DOI: 10.1021/acsnano.8b00537.

127. Ramaswamy, S., Colangelo, C., and Markram, H. (2018). Data-Driven Modeling of Cholinergic Modulation of Neural Microcircuits: Bridging Neurons, Synapses and Network Activity. Front. Neural Circuits 12, 77–77.
https://www.frontiersin.org/articles/10.3389/fncir.2018.00077/full

126. Ramaswamy, S., Muller, E., Reimann, M., and Markram, H. (2018). Microcircuitry of the neocortex. In Handbook of Brain Microcircuits, Section 1: Neocortex, Chapter 3, G.M. Shepherd, and S. Grillner, eds. (Oxford University Press), p. 35-46.
https://books.google.ch/books?id=n8M9DwAAQBAJ.

125. Shardlow, M., Ju, M., Li, M., O’Reilly, C., Iavarone, E., McNaught, J., and Ananiadou, S. (2018). A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience. Neuroinformatics. 15 Nov 2018, 1-6.
DOI: 10.1007/s12021-018-9404-y.

124. Wybo, W., Torben-Nielsen, B., and Gewaltig, M.-O. (2018). Dynamic compartmentalization in neurons enables branch specific learning. bioRxiv.
https://www.biorxiv.org/content/early/2018/01/08/244772.

123. Abdellah, M., Bilgili, A., Eilemann, S., Markram, H., and Schürmann, F. (2017). A Physically Plausible Model for Rendering Highly Scattering Fluorescent Participating Media. arXiv, v2, 12 June 2017.
https://arxiv.org/abs/1706.03024v2.

122. Abdellah, M., Bilgili, A., Eilemann, S., Shillcock, J., Markram, H., and Schürmann, F. (2017). Bio-physically plausible visualization of highly scattering fluorescent neocortical models for in silico experimentation. BMC Bioinformatics 18, 62.
DOI: 10.1186/s12859-016-1444-4

121. Abdellah, M., Hernando, J., Antille, N., Eilemann, S., Markram, H., and Schürmann, F. (2017). Reconstruction and visualization of large-scale volumetric models of neocorticalcircuits for physically-plausible in silico optical studies. BMC Bioinformatics 18, 402.
DOI: 10.1186/s12859-017-1788-4

120. Deitcher, Y., Eyal, G., Kanari, L., Verhoog, M.B., Atenekeng Kahou, G.A., Mansvelder, H.D., de Kock, C.P.J., and Segev, I. (2017). Comprehensive Morpho-Electrotonic Analysis Shows 2 Distinct Classes of L2 and L3 Pyramidal Neurons in Human Temporal Cortex. Cereb. Cortex 27, 5398–5414.
DOI: 10.1093/cercor/bhx226

119. Doron, M., Chindemi, G., Muller, E., Markram, H., and Segev, I. (2017). Timed SynapticInhibition Shapes NMDA Spikes, Influencing Local Dendritic Processing and Global I/OProperties of Cortical Neurons. Cell Rep 21, 1550–1561.
DOI: 10.1016/j.celrep.2017.10.035

118. Eilemann, S., Abdellah, M., Antille, N., Bilgili, A., Chevtchenko, G., Dumusc, R., Favreau, C., Hernando, J., Nachbaur, D., Podhajski, P., Villafranca, J., Schürmann, F. (2017). From Big Data to Big Displays High-Performance Visualization at Blue Brain. In High Performance Computing, J.M. Kunkel, R. Yokota, M. Taufer, and J. Shalf, eds. (Springer International Publishing), pp. 662–675.
DOI: 10.1007/978-3-319-67630-2_47

117. Ewart, T., Planas, J., Cremonesi, F., Langen, K., Schürmann, F., and Delalondre, F. (2017). Neuromapp: A Mini-application Framework to Improve Neural Simulators. In High Performance Computing, J.M. Kunkel, R. Yokota, P. Balaji, and D. Keyes, eds. (Springer International Publishing), pp. 181–198.
DOI: 10.1007/978-3-319-58667-0_10

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85. M. Reimann, E.Muller, S.Ramaswamy, H.Markram: An Algorithm to Predict the Connectome of Neural Microcircuits. 2015. Frontiers in Neural Circuits 9 2015, 28.
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84. A. Devresse, F. Delalondre, F. Schürmann, Blue Brain Project Fully Automated Workflows and Ecosystem to guarantee Scientific Result Reproducibility across Platforms, Software Environment and Systems. International Conference for High Performance Computing, Networking, Storage and Analysis 2015, Austin, Texas.
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83. T. Ewart, S. Yates, F. Cremonesi, P. Kumbhar, F. Schuermann, F. Delalondre, Performance Evaluation of the IBM POWER8 system to Support Computational Neuroscientific Application Using Morphologically Detailed Neurons. PMBS15 Workshop, Supercomputing 2015, Austin, Texas.
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82. M.Abdellah, A.Bilgilli, S.Eilemann, H.Markram, F.Schürmann : Physically-based in silico light sheet microscopy for visualizing fluorescent brain models. BMC Bioinformatics. 2015 Aug 13;16 Suppl 11:S8
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81. V.Delattre, D.Keller, M.Perich, H.Markram, E.B.Muller: Network-timing-dependent plasticity. Front Cell Neurosci. 2015 Ju 9;9:220.
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80. Anastassiou CA, Perin R, Buzsáki G, Markram H, Koch C. Cell type- and activity-dependent extracellular correlates of intracellular spiking. J Neurophysiol. 2015 Jul;114(1):608-23. doi: 10.1152/jn.00628.2014.
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79. S.Ramaswamy, H.Markram: Anatomy and Physiology of the thick-tufted layer 5 pyramidal neuron, Front Cell Neurosci. 2015; 9:233.
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78. D.Keller, N.Babai, O.Kochubey, Y.Han, H.Markram, F.Schürmann, R.Schneggenburger: An Exclusion Zone for Ca2+ Channels around Docked Vesicles Explains Release Control by Multiple Channels at a CNS Synapse, PLoS Comput Biol. 2015 May 7;11(5):e1004253.
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77. Costantini I, Ghobril JP, Di Giovanna AP, Allegra Mascaro AL, Silvestri L, Müllenbroich MC, Onofri L, Conti V, Vanzi F, Sacconi L, Guerrini R, Markram H, Iannello G, Pavone FS A versatile clearing agent for multi-modal brain imaging. Scientific Reports. 2015 May 7;5:9808.
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76. Frackowiak R, Markram H. The future of human cerebral cartography: a novel approach. Philos Trans R Soc Lond B Biol Sci. 2015 May 19;370(1668). pii: 20140171.
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75. Vannucci, L., Ambrosano, A., Cauli, N., Albanese, U., Falotico, E., Ulbrich, S., Pfotzer, L., Hinkel, G., Denninger, O., Peppicelli, D., Guyot, L., Von Arnim, A., Deser, S., Maier, P., Dillman, R., Klinker, G., Levi, P., Knoll, A., Gewaltig, M.-O., and Laschi, C. (2015). A visual tracking model implemented on the iCub robot as a use case for a novel neurorobotic toolkit integrating brain and physics simulation. In 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 1179–1184.
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74. X.Vasques, R.Richardet, SL.Hill, D.Slater, J-C.Chappelier, E.Pralong. J.Bloh, B.Draganski, L.Cif: Automatic target validation based on neuroscientific literature mining for tractography, Front Neuroanat. 2015 May 27;9:66.
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72. S. Ramaswamy and E. Muller, Cell-type specific modulation of neocortical UP and DOWN states. Frontiers in Cellular Neuroscience, 9:370, 2015.
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71. S. Ramaswamy, Exciting times for inhibition: GABAergic synaptic transmission in dentate gyrus interneuron networks. Frontiers in Neural Circuits, 9:13, 2015.
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65. F.Schürmann, F.Delalondre, P.S.Kumbhar, J.Biddiscombe, M.Gila, D.Tacchella, A.Curioni, B.Metzler, P.Morjan, J.Fenkes, M.M.Franceschini, R.S.Germain, L.Schneidenbach, T.J.C.Ward, B.G.Fitch: 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).
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64. T.Ewart, F.Delalondre, F.Schürmann: 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).
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63. S.Muralidhar, Y.Wang, H.Markram: Synaptic and cellular organization of layer 1 of the developing rat somatosensory cortex. Front Neuroanat. 2014 Jan 16;7:52.
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62. F.Tauheed, T.Heinis, F.Schürmann, H.Markram, A.Ailamaki : OCTOPUS: Efficient Query Execution on Dynamic Mesh Datasets, In Proceedings of the 30th IEEE International Conference on Data Engineering. Chicago, USA, March 2014.
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60. 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).
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59. Adaszewski, S. (2014). Mynodbcsv: lightweight zero-config database solution for handling very large C SV files. PLoS ONE 9, e103319.
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56. Toledo-Rodriguez, M., 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.]
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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.
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54. E. Hay, F. Schürmann, H. Markram, I. Segev: Preserving axosomatic spiking features despite diverse dendritic morphology. J Neurophysiol, 109(12), 2972-2981, 2013.
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53. J.B. Hernando, J. Biddiscombe, B. Bohara, S. Eilemann, F. Schürmann: Practical parallel rendering of detailed neuron simulations, EGPGV 2013.
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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.
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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.
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49. ER.Kandel, H.Markram, PM.Matthews, R.Yuste, C.Koch: Neuroscience thinks big (and collaboratively). 2013. Nat Rev Neurosci. 14(9):659-64.
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48. J.DeFelipe et al. [42 authors]: New insights into the classification and nomenclature of cortical GABAergic interneurons. 2013. Nat Rev Neurosci. 14(3):202-16.
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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).
DOI:

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.  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. 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.
ISSN:0018-8646

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.01.1.1.001.2007

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.
DOI:

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