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77. Costantini I., Ghobril J., P., Di Giovanna A., P., Allegra Mascaro A., L., Silvestri L., Müllenbroich M., C., Onofri L., Conti V., Vanzi F., Sacconi L., Guerrini R., Markram H., Iannello G., & Pavone F., S. (2015). A versatile clearing agent for multi-modal brain imaging. Scientific Reports. May 7;5:9808.
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76. Frackowiak R., & Markram H. (2015). The future of human cerebral cartography: a novel approach. Philos Trans R Soc Lond B Biol Sci. May 19;370(1668). pii: 20140171.
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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., & 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. Vasques, X., Richardet, R., Hill, S.,L., Slater, D., Chappelier, J.,-C., Pralong, E., Bloh, J., Draganski, B., & Cif L. (2015). Automatic target validation based on neuroscientific literature mining for tractography, Front Neuroanat. May 27;9:66.
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73. Richardet, R., Chappelier, J.,-C., Telefont, M., & Hill S. (2015). Large-scale extraction of brain connectivity from the neuroscientific literature, Bioinformatics. May; 31(10):1640-1647.
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72. Ramaswamy, S., & Muller, E. (2015). Cell-type specific modulation of neocortical UP and DOWN states. Frontiers in Cellular Neuroscience, 9:370,
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71. Ramaswamy, S. (2015). Exciting times for inhibition: GABAergic synaptic transmission in dentate gyrus interneuron networks. Frontiers in Neural Circuits, 9:13,
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70. Muller, E., Bednar, J., A., Diesmann M., Gewaltig, M.-O., Hines, M., & Davison, A., P., (2015). Python in Neuroscience. Frontiers in Neuroinformatics, 9.
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69. Wybo, W., A., M., Boccalini, D., Torben-Nielsen, B., & Gewaltig, M.,O. (2015). A Sparse Reformulation of the Green’s Function Formalism Allows Efficient Simulations of Morphological Neuron Models. Neural Comput 27, 2587–2622.
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68. Tiesinga, P., Bakker, R., Hill, S., & Bjaalie, J.G. (2015). Feeding the human brain model. Curr. Opin. Neurobiol. 32, 107–114.
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67. Jolivet, R., Coggan, J.S., Allaman, I., & Magistretti, P.J. (2015). Multi-ti66. E.Hay and I.Segev: Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits. Cerebral Cortex, 2014 Sep 9.
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66. Hay, E., & Segev, I. (2015). Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits. Cerebral Cortex, 2014 Sep 9
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65. Schürmann, F., Delalondre, F., Kumbhar, P.,S., Biddiscombe, J., Gila, M., Tacchella, D., Curioni, A., Metzler, B., Morjan, P., Fenkes, J., Franceschini, M., M., Germain, R., S., Schneidenbach, L., Ward, T., J., C., & Fitch B., G., Rebasing I/O for Scientific Computing: Leveraging Storage Class Memory in an IBM BlueGene/Q Supercomputer. In J.M. Kunkel, T. Ludwig, and H.W. Meuer (Eds.): ISC 2014, LNCS 8488, pp. 331–347. Springer International Publishing Switzerland (2014).
DOI: 10.1007/978-3-319-07518-1_21
64. Ewart, T., Delalondre, F., & Schürmann F., Cyme: A Library Maximizing SIMD Computation on User-Defined Containers. In J.M. Kunkel, T. Ludwig, and H.W. Meuer (Eds.): ISC 2014, LNCS 8488, pp. 440–449. Springer International Publishing Switzerland. (2014).
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63. Muralidhar, S., Wang, Y., & Markram H. Synaptic and cellular organization of layer 1 of the developing rat somatosensory cortex. Front Neuroanat. 2014 Jan 16;7:52.
DOI: 10.3389/fnana.2013.00052
62. Tauheed, F.,Heinis, T., Schürmann, F., Markram, H., & Ailamaki A. OCTOPUS: Efficient Query Execution on Dynamic Mesh Datasets, In Proceedings of the 30th IEEE International Conference on Data Engineering. Chicago, USA, March 2014.
DOI: 10.1109/ICDE.2014.6816718
61. Gewaltig, MO., & Cannon, R. Current practice in software development for computational neuroscience and how to improve it. 2014. PLoS Comput Biol. 10(1).
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60. DeFelipe, J., Garrido, E., & Markram H. The death of Cajal and the end of scientific romanticism and individualism. Trends Neurosci. 37(10):525-7 (2014).
DOI: 10.1016/j.tins.2014.08.002
59. Adaszewski, S. (2014). Mynodbcsv: lightweight zero-config database solution for handling very large C SV files. PLoS ONE. 9, e103319.
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58. Babai, N., Kochubey, O., Keller, D., & Schneggenburger, R. (2014). An alien divalent ion reveals a major role for Ca2+ buffering in controlling slow transmitter release. J. Neurosci. 34, 12622–12635.
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57. Kriener, B., Enger, H., Tetzlaff, T., Plesser, H.E., Gewaltig, M.-O., & Einevoll, G.T. (2014). Dynamics of self-sustained asynchronous-irregular activity in random networks of spiking neurons with strong synapses. Front Comput Neurosci. 8, 136.
DOI: 10.3389/fncom.2014.00136
56. Toledo-Rodriguez, M., & Markram, H. (2014). New Edition: Single-cell RT-PCR, a technique to decipher the electrical, anatomical, and genetic determinants of neuronal diversity. In: Martina M., Taverna S. (eds)Patch-Clamp Methods and Protocols. Methods in Molecular Biology (Methods and Protocols). In Methods in Molecular Biology, pp. 143–158. [For accessible earlier version see Toledo-Rodriquez et al 2007.]
DOI: 10.1007/978-1-4939-1096-0_8
55. Reimann, M., W., Anastassiou, C., A., Perin, R., Hill, S., L., Markram, H., & Koch C. A biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents. Neuron, 79(2), 375-390, 2013.
DOI: 0.1016/j.neuron.2013.05.023
54. Hay, E., Schürmann, F., Markram, H., & Segev, I. Preserving axosomatic spiking features despite diverse dendritic morphology. J Neurophysiol, 109(12), 2972-2981, 2013.
DOI: 10.1152/jn.00048.2013
53. Hernando, J., B., Biddiscombe, J., Bohara, B., Eilemann, S., & Schürmann F. Practical parallel rendering of detailed neuron simulations, EGPGV. 2013.
DOI: 10.2312/EGPGV/EGPGV13/049-056
52. Perin, R., Telefont, M., & Markram H.Computing the size and number of neuronal clusters in local circuits, Front Neuroanat. 2013;7:1. Epub 2013 Feb 19.
DOI: 10.3389/fnana.2013.00001
51. Loebel, A., LeBe, JV., Richardson, MJ., Markram, H., & Herz A. Matched pre- and post-synaptic changes underlie synaptic plasticity over long time scales. 2013. J Neurosci. 33(15):6257-66.
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50. Markram H. Seven challenges for Neuroscience. 2013. Functional Neurology. 28(3):145-51.
DOI: 10.11138/FNeur/2013.28.3.145
49. Kandel, ER., Markram, H., Matthews, PM., Yuste, & Koch C. Neuroscience thinks big (and collaboratively). 2013. Nat Rev Neurosci. 14(9):659-64.
DOI: 10.1038/nrn3578
48. J.DeFelipe et al. [42 authors]: New insights into the classification and nomenclature of cortical GABAergic interneurons. 2013. Nat Rev Neurosci. 14(3):202-16.
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47. Wybo, W.A.M., Stiefel, K.M., & Torben-Nielsen, B. (2013). The Green’s function formalism as a bridge between single- and multi-compartmental modeling. Biol Cybern. 107, 685–694.
DOI: 10.1007/s00422-013-0568-0
46. Druckmann, S., Hill, S., Schürmann, F., Markram, H., & Segev I. A Hierarchical Structure of Cortical Interneuron Electrical Diversity Revealed by Automated Statistical Analysis, Cerebral Cortex, (2012), doi: 10.1093/cercor/bhs290.
DOI: 10.1093/cercor/bhs290
45 Markram, H., Gerstner, W., & Sjöström, P.J. (2012). Editorial Article: Spike-timing-dependent plasticity: a comprehensive overview. Front Synaptic Neurosci. 4, 2.
DOI: 10.3389/fnsyn.2012.00002
44. Tauheed, F., Biveinis, L., Heinis, T., Schurmann, F., Markram, H., & Ailamaki, A. (2012a). Accelerating Range Queries for Brain Simulations. In Proceedings of the 2012 IEEE 28th International Conference on Data Engineering, (Washington, DC, USA: IEEE Computer Society), pp. 941–952.
DOI: 10.1109/ICDE.2012.56
43. Hernando, J., Schürmann, F., & Pastor, L. (2012). Towards real-time visualization of detailed neural tissue models: View frustum culling for parallel rendering. In IEEE Symposium on Biological Data Visualization (BioVis), (IEEE), pp. 25–32.
DOI: 10.1109/BioVis.2012.6378589
42. Hill, S. L., Wang, Y., Riachi, I., Schürmann, F., & Markram, H. (2012). Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits. Proceedings of the National Academy of Sciences, 109(42). https://doi.org/10.1073/pnas.1202128109.
41. Gidon A., & Segev I. Principles governing the operation of synaptic inhibition in dendrites, Neuron, 2012 Jul 26;75(2):330-41.
DOI: 10.1016/j.neuron.2012.05.015
40. Tauheed, F., Heinis, Schürmann, F.,Markram, H., & SCOUT A., A. Prefetching of Latent Structure Following Queries, VLDB 2012.
DOI: 10.14778/2350229.2350267
39. Khazen, G.,Hill, S., L., Schürmann F., & Markram H. Combinatorial Expression Rules of Ion Channel Genes in Juvenile Rat (Rattus norvegicus) Neocortical Neurons, PLoS One, 7(4): e34786.
DOI: 10.1371/journal.pone.0034786
38. Eilemann, S., Bilgili, A., Abdellah, M., Hernando, J., Makhinya, M., Pajarola, R., & Schürmann F. Parallel Rendering on Hybrid Multi-GPU Clusters, EGPGV 2012.
DOI: 10.2312/EGPGV/EGPGV12/109-117
37. Lasserre, S., Hernando, J., Hill, S., Schürmann, F., de Miguel Anasagasti, P., Abou Jaoudé, G., & Markram H. A Neuron Mesh Representation for Visualization of Electrophysiological Simulations, IEEE Transactions on Visualization and Computer Graphics, 18 (2): p. 214-217.
DOI: 10.1109/TVCG.2011.55
36. S.Ramaswamy, S.L.Hill, J.G.King, F.Schürmann, Y.Wang, and H.Markram: Intrinsic Morphological Diversity of Thick-tufted Layer 5 Pyramidal Neurons Ensures Robust and Invariant Properties of in silico Synaptic Connections. J Physiol. 2012 Feb 15;590(Pt 4):737-52. Epub 2011 Nov 14. DOI: 10.1113/jphysiol.2011.219576
35. Ranjan, R., Khazen, G., Gambazzi, L., Ramaswamy, S., Hill, S., L., Schürmann, F., & Markram H. Channelpedia: an integrative and interactive database for ion channels, Front. Neuroinform. 2011. 5:36.
DOI: 10.3389/fninf.2011.00036
34. Hines, M., Kumar, S., & Schürmann F. Comparison of neuronal spike exchange methods on a Blue Gene/P supercomputer. Front. Comput. Neurosci. 2011. 5:49.
DOI: 10.3389/fncom.2011.00049
33. Hay, E., Hill, S.,L., Schürmann, F., Markram, H., & Segev I. Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties. PLoS Computational Biology. 2011, 7(7): e1002107.
DOI: 10.1371/journal.pcbi.1002107
32. Druckmann, S., Berger, T.,K., Schürmann, F., Hill, S., L., Markram, H., & Segev I., Effective stimuli for constructing reliable neuron models, Plos Computational Biology, 2011, 7(8): e1002133.
DOI: 10.1371/journal.pcbi.1002133
31. Perin, R., Berger, T., K., & Markram H. A synaptic organizing principle for cortical neuronal groups, PNAS, 2011, 108 (12).
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30. Romand, S., Wang, Y., Toledo-Rodriguez, M., & Markram H. Morphological development of thick-tufted layer v pyramidal cells in the rat somatosensory cortex, Front Neuroanat. 2011 5:5.
DOI: 10.3389/fnana.2011.00005
29. Anastassiou, CA., Perin, R., Markram, H., & Koch C. Ephaptic coupling of cortical neurons, Nat Neurosci. 2011 Feb;14(2):217-23.
DOI: 10.1038/nn.2727
28. Markram, H., Gerstner, W., & Sjöström PJ. A history of spike-timing-dependent plasticity. Front Synaptic Neurosci. 2011;3:4. Epub 2011 Aug 29.
DOI: 10.3389/fnsyn.2011.00004
27. Markram, H., & Perin R. Innate neural assemblies for lego memory. Front Neural Circuits. 2011;5:6. Epub 2011 May 16.
DOI: 10.3389/fncir.2011.00006
26. Berger, TK., Silberberg, G., Perin, R., & Markram H. Brief bursts self-inhibit and correlate the pyramidal network, PLoS Biol. 2010 Sep 7;8(9).
DOI: 10.1371/journal.pbio.1000473
25. Bar-Ilan, L., Gidon, A., & Segev I. Inter-regional synaptic competition in neurons with multiple STDP-inducing signals, J Neurophysiol (December 1, 2010).
DOI: 10.1152/jn.00612.2010.
24. Loebel, A., Silberberg, G., Helbig, D., Markram, H., Tsodyks, M., & Richardson MJ. Multiquantal release underlies the distribution of synaptic efficacies in the neocortex, Front Comput Neurosci. 2009; 3:27.
DOI: 10.3389/neuro.10.027.2009
23. Berger, TK. Perin, R., Silberberg, G., & Markram H. Frequency-dependent disynaptic inhibition in the pyramidal network: a ubiquitous pathway in the developing rat neocortex, J Physiol. 2009 Nov 15;587(Pt 22):5411-25.
DOI: 10.1113/jphysiol.2009.176552
22. King, J., G., Hines, M., Hill, S., Goodman, P., H., Markram, H., & Schürmann F. A component-based extension framework for large-scale parallel simulations in NEURON , Front Neuroinformatics, 3:10.
DOI: 10.3389/neuro.11.010.2009
21. Anwar H., Riachi I., Schürmann F., & Markram H. (2009). “An approach to capturing neuron morphological diversity,” in Computational Neuroscience: Realistic Modeling for Experimentalistsed. De Schutter E., editor. (Cambridge: The MIT Press) 211–232. https://mitpress.mit.edu/books/computational-modeling-methods-neuroscientists.
ISBN 978-0-262-01327
20. Jolivet, R., Schürmann, F., Berger, T. K., Naud, R., Gerstner, W., & Roth, A. (2008). The quantitative single-neuron modeling competition. Biological Cybernetics, 99(4), 417-426. https://doi.org/10.1007/s00422-008-0261-x.
19. Kozloski, J., Sfyrakis, K., Hill, S., Schürmann, F., Peck, C., & Markram H. Identifying, tabulating, and analyzing contacts between branched neuron morphologies, IBM Journal of Research and Development, Vol 52, Number 1/2, 2008.
ISSN:0018-8646
18. Hines, M., Eichner, H., & Schürmann F. Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors, J. Comput. Neurosci., 25(1):203-10, 2008.
DOI: 10.1007/s10827-007-0073-3
17. Hines, M., Markram, H., & Schürmann F. Fully Implicit Parallel Simulation of Single Neurons, J. Comput. Neurosci., 25(3):439-48, 2008.
DOI: 10.1007/s10827-008-0087-5
16. Druckmann, S., Berger, T., Hill, S., Schürmann, F., Markram, H., & Segev I. Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data, Biol Cybern, 99(4-5):371-9, 2008.
DOI: 10.1007/s00422-008-0269-2
15. Calì, C., Berger, TK., Pignatelli, M., Carleton, A., Markram, H., & Giugliano M. Inferring connection proximity in networks of electrically coupled cells by subthreshold frequency response analysis, J Comput Neurosci. 2008 Jun;24(3):330-45. Epub 2007 Nov 28.
DOI: 10.1007/s10827-007-0058-2
14. Melamed, O., Barak, O., Silberberg, G., Markram, H., & Tsodyks M. Slow oscillations in neural networks with facilitating synapses, J Comput Neurosci. 2008 Oct;25(2):308-16.
DOI: 10.1007/s10827-008-0080-z
13. Ascoli, GA., Alonso-Nanclares L., Anderson SA., Barrionuevo G., Benavides-Piccione R., Burkhalter A., Buzsáki G., Cauli B., Defelipe J., Fairén A., Feldmeyer D., Fishell G., Fregnac Y., Freund TF., Gardner D., Gardner EP., Goldberg JH., Helmstaedter M., Hestrin S., Karube F., Kisvárday ZF., Lambolez B., Lewis DA., Marin O., Markram H., Muñoz A., Packer A., Petersen CC., Rockland KS., Rossier J., Rudy B., Somogyi P., Staiger JF., Tamas G., Thomson AM., Toledo-Rodriguez M., Wang Y., West DC., & Yuste R. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex, Nat Rev Neurosci. 2008 Jul;9(7):557-68.
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12. Markram, H. Fixing the location and dimensions of functional neocortical columns, HFSP J. 2008 Jun;2(3):132-5.
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11. Silberberg, G., & Markram, H. Disynaptic inhibition between neocortical pyramidal cells mediated by Martinotti cells, Neuron. 2007 Mar 1;53(5):735-46.
DOI: 10.1016/j.neuron.2007.02.012
10. Markram, H. Bioinformatics: industrializing neuroscience. Nature. 2007 Jan 11;445(7124):160-1.
DOI: 10.1038/445160a
9. Abid, A., Jan, A., Francioli, L., Sfyrakis, K., & Schürmann F. Keyword Based Indexing and Searching over Storage Resource Broker. OTM Conferences, 2007, Proceedings, Part II. Lecture Notes in Computer Science 4804 Springer. 2007, ISBN 978-3-540-76835-7, pp. 1233-43.
DOI: 10.1007/978-3-540-76843-2_6
8. Druckmann, S.,Banitt, Y., Gidon, A., Schürmann, F., Markram, H., & Segev I. A Novel Multiple Objective Optimization Framework for Constraining Conductance-Based Neuron Models by Experimental Data, Frontiers in Neuroscience, Vol. 1, Issue 1, 2007.
DOI: 10.3389/neuro.01.1.1.001.2007
7. Toledo-Rodriguez M., & Markram H. Single-cell RT-PCR, a technique to decipher the electrical, anatomical, and genetic determinants of neuronal diversity, Methods Mol Biol. 2007;403:123-39.
DOI: 10.1007/978-1-59745-529-9_8
6. Le Bé, JV., Silberberg, G., Wang, Y., & Markram H. Morphological, electrophysiological, and synaptic properties of corticocallosal pyramidal cells in the neonatal rat neocortex, Cereb Cortex. 2007 Sep;17(9):2204-13.
DOI: 10.1093/cercor/bhl127
5. Migliore, M., Cannia, C., Lytton, W.,W., Markram, H., & Hines M., L., Parallel network simulations with NEURON, J Comput Neurosci. 2006 Oct;21(2):119-29.
DOI: 10.1007/s10827-006-7949-5
4. Markram H. The blue brain project. Nat Rev Neurosci. 7, 153-160, 2006.
DOI: 10.1038/nrn1848
3. Wang, Y., Markram, H., Goodman, PH., Berger, TK., & Goldman-Rakic, J.Ma PS. Heterogeneity in the pyramidal network of the medial prefrontal cortex, Nat Neurosci. 2006 Apr;9(4):534-42.
DOI: 10.1038/nn1670
2. Le Bé, JV., & Markram H. Spontaneous and evoked synaptic rewiring in the neonatal neocortex, PNAS. 2006 Aug 29;103(35):13214-9.
DOI:
1. Muhammad, A., J., & Markram, H.NEOBASE: Databasing the Neocortical Microcircuit, Stud Health Technol Inform. 2005;112:167-77.