Biological Cybernetics

, Volume 104, Issue 4–5, pp 263–296 | Cite as

A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems

  • Daniel Brüderle
  • Mihai A. Petrovici
  • Bernhard Vogginger
  • Matthias Ehrlich
  • Thomas Pfeil
  • Sebastian Millner
  • Andreas Grübl
  • Karsten Wendt
  • Eric Müller
  • Marc-Olivier Schwartz
  • Dan Husmann de Oliveira
  • Sebastian Jeltsch
  • Johannes Fieres
  • Moritz Schilling
  • Paul Müller
  • Oliver Breitwieser
  • Venelin Petkov
  • Lyle Muller
  • Andrew P. Davison
  • Pradeep Krishnamurthy
  • Jens Kremkow
  • Mikael Lundqvist
  • Eilif Muller
  • Johannes Partzsch
  • Stefan Scholze
  • Lukas Zühl
  • Christian Mayr
  • Alain Destexhe
  • Markus Diesmann
  • Tobias C. Potjans
  • Anders Lansner
  • René Schüffny
  • Johannes Schemmel
  • Karlheinz Meier
Original Paper

Abstract

In this article, we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware–software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results.

Keywords

Neuromorphic VLSI Hardware Wafer scale Software Modeling Computational neuroscience PyNN 

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References

  1. Aviel Y, Mehring C, Abeles M, Horn D (2003) On embedding synfire chains in a balanced network. Neural Comput 15(6): 1321–1340PubMedCrossRefGoogle Scholar
  2. Berge HKO, Häfliger P (2007) High-speed serial AER on FPGA. In: Proceedings of the 2007 IEEE international symposium on circuits and systems (ISCAS), pp 857–860Google Scholar
  3. Bill J, Schuch K, Brüderle D, Schemmel J, Maass W, Meier K (2010) Compensating inhomogeneities of neuromorphic VLSI devices via short-term synaptic plasticity. Front Comp Neurosci 4(129)Google Scholar
  4. Binzegger T, Douglas RJ, Martin KAC (2004) A quantitative map of the circuit of cat primary visual cortex. J Neurosci 24(39): 8441–8453PubMedCrossRefGoogle Scholar
  5. Bontorin G, Renaud S, Garenne A, Alvado L, Le Masson G, Tomas J (2007) A real-time closed-loop setup for hybrid neural networks. In: Proceedings of the 29th annual international conference of the IEEE engineering in medicine and biology society (EMBS2007)Google Scholar
  6. BrainScaleS (2010) Project website. http://www.brainscales.eu
  7. Brette R, Gerstner W (2005) Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J Neurophysiol 94: 3637–3642PubMedCrossRefGoogle Scholar
  8. Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, Goodman PH, Harris FC Jr, Zirpe M, Natschlager T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A, Rochel O, Vieville T, Muller E, Davison AP, El Boustani S, Destexhe A (2006) Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci 23(3): 349–398CrossRefGoogle Scholar
  9. Brüderle D (2009) Neuroscientific modeling with a mixed-signal VLSI hardware system. PhD thesis, Ruprecht-Karls-Universität, HeidelbergGoogle Scholar
  10. Brüderle D, Müller E, Davison A, Muller E, Schemmel J, Meier K (2009) Establishing a novel modeling tool: a python-based interface for a neuromorphic hardware system. Front Neuroinform 3(17)Google Scholar
  11. Brüderle D, Bill J, Kaplan B, Kremkow J, Meier K, Müller E, Schemmel J (2010) Simulator-like exploration of cortical network architectures with a mixed-signal VLSI system. In: Proceedings of the 2010 IEEE international symposium on circuits and systems (ISCAS), pp 2784–2787Google Scholar
  12. Brunel N (2000) Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J Comput Neurosci 8(3): 183–208PubMedCrossRefGoogle Scholar
  13. Burkitt A, Gilson M, Hemmen J (2007) Spike-timing-dependent plasticity for neurons with recurrent connections. Biol Cybern 96(5): 533–546PubMedCrossRefGoogle Scholar
  14. Buxhoeveden D, Casanova M (2002) The minicolumn and evolution of the brain. Brain Behav Evol 60: 125–151PubMedCrossRefGoogle Scholar
  15. Connors B, Gutnick M (1990) Intrinsic firing patterns of diverse neocortical neurons. Trends Neurosci 13: 99–104PubMedCrossRefGoogle Scholar
  16. Costas-Santos J, Serrano-Gotarredona T, Serrano-Gotarredona R, Linares-Barranco B (2007) A spatial contrast retina with on-chip calibration for neuromorphic spike-based AER vision systems. IEEE Trans Circuits Syst 54(7): 1444–1458CrossRefGoogle Scholar
  17. Dante V, Del Giudice P, Whatley A (2005) Hardware and software for interfacing to address-event based neuromorphic systems. Neuromorp Eng 2(1): 5–6Google Scholar
  18. Daouzli A, Saighi S, Buhry L, Bornat Y, Renaud S (2008) Weights convergence and spikes correlation in an adaptive neural network implemented on vlsi. In: Proceedings of the international conference on bio-inspired systems and signal processing (BIOSIGNALS), pp 286–291Google Scholar
  19. Davison AP, Frégnac Y (2006) Learning crossmodal spatial transformations through spike-timing-dependent plasticity. J Neurosci 26(21): 5604–5615PubMedCrossRefGoogle Scholar
  20. Davison AP, Brüderle D, Eppler JM, Kremkow J, Muller E, Pecevski D, Perrinet L, Yger P (2008) PyNN: a common interface for neuronal network simulators. Front Neuroinform 2:(11)Google Scholar
  21. Delbrück T, Liu SC (2004) A silicon early visual system as a model animal. Vis Res 44(17): 2083–2089PubMedCrossRefGoogle Scholar
  22. Destexhe A (2009) Self-sustained asynchronous irregular states and Up/Down states in thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons. J Comput Neurosci 3: 493–506CrossRefGoogle Scholar
  23. Destexhe A, Contreras D, Steriade M (1998) Mechanisms underlying the synchronizing action of corticothalamic feedback through inhibition of thalamic relay cells. J Neurophysiol 79: 999–1016PubMedGoogle Scholar
  24. Diesmann M, Gewaltig MO, Aertsen A (1999) Stable propagation of synchronous spiking in cortical neural networks. Nature 402: 529–533PubMedCrossRefGoogle Scholar
  25. Douglas RJ, Martin KAC (2004) Neuronal circuits of the neocortex. Annu Rev Neurosci 27: 419–451PubMedCrossRefGoogle Scholar
  26. Ehrlich M, Mayr C, Eisenreich H, Henker S, Srowig A, Grübl A, Schemmel J, Schüffny R (2007) Wafer-scale VLSI implementations of pulse coupled neural networks. In: Proceedings of the international conference on sensors, circuits and instrumentation systems (SSD-07)Google Scholar
  27. Ehrlich M, Wendt K, Zühl L, Schüffny R, Brüderle D, Müller E, Vogginger B (2010) A software framework for mapping neural networks to a wafer-scale neuromorphic hardware system. In: Proceedings of ANNIIP 2010, pp 43–52Google Scholar
  28. El Boustani S, Pospischil M, Rudolph-Lilith M, Destexhe A (2007) Activated cortical states: experiments, analyses and models. J Physiol (Paris) 101: 99–109CrossRefGoogle Scholar
  29. Eppler JM, Helias M, Muller E, Diesmann M, Gewaltig MO (2008) PyNEST: a convenient interface to the NEST simulator. Front Neuroinform 2: 12CrossRefGoogle Scholar
  30. FACETS (2010) Fast analog computing with emergent transient states—project website. http://www.facets-project.org
  31. Fairhurst G (2002) RFC 3366: advice to link designers on link automatic repeat request (ARQ). http://www.rfc-editor.org/rfc/rfc3366.txt
  32. Fieres J, Schemmel J, Meier K (2008) Realizing biological spiking network models in a configurable wafer-scale hardware system. In: Proceedings of the 2008 international joint conference on neural networks (IJCNN)Google Scholar
  33. Friedmann S (2009) Extending a hardware neural network beyond chip boundaries. Diploma thesis (English), Ruprecht-Karls-Universität, Heidelberg, HD-KIP-09-41, http://www.kip.uni-heidelberg.de/Veroeffentlichungen/details.php?id=1938
  34. Fu Z, Culurciello E, Lichtsteiner P, Delbrück T (2008) Fall detection using an address-event temporal contrast vision sensor. In: Proceedings of the 2008 IEEE international symposium on circuits and systems (ISCAS), pp 424–427Google Scholar
  35. Gewaltig MO, Diesmann M (2007) NEST (neural simulation tool). Scholarpedia 2(4): 1430CrossRefGoogle Scholar
  36. Gomez-Rodriguez F, Miro-Amarante L, Diaz-del Rio F, Linares-Barranco A, Jimenez G (2010) Real time multiple objects tracking based on a bio-inspired processing cascade architecture. In: Proceedings of 2010 IEEE international symposium on circuits and systems (ISCAS), pp 1399–1402Google Scholar
  37. Goodman D, Brette R (2008) Brian: a simulator for spiking neural networks in Python. Front Neuroinform 2(5)Google Scholar
  38. Gütig R, Aharonov R, Rotter S, Sompolinsky H (2003) Learning input correlations through nonlinear temporally asymmetric hebbian plasticity. J Neurosci 23(9): 3697–3714PubMedGoogle Scholar
  39. Häfliger P (2007) Adaptive WTA with an analog VLSI neuromorphic learning chip. IEEE Trans Neural Netw 18(2): 551–572PubMedCrossRefGoogle Scholar
  40. Hartmann S, Schiefer S, Scholze S, Partzsch J, Mayr C, Henker S, Schüffny R (2010) Highly integrated packet-based AER communication infrastructure with 3Gevent/s throughput. In: IEEE international conference on electronics, circuits and systems, ICECS, Dec 2010, pp 952–955Google Scholar
  41. Hines ML, Carnevale NT (2006) The NEURON book. Cambridge University Press, CambridgeGoogle Scholar
  42. Hines ML, Davison AP, Muller E (2009) NEURON and Python. Front Neuroinform 3(1)Google Scholar
  43. Horak R (2007) Telecommunications and data communications handbook. Wiley-Interscience, New YorkCrossRefGoogle Scholar
  44. Hunter JD (2007) Matplotlib: a 2D graphics environment. IEEE Comput Sci Eng 9(3): 90–95Google Scholar
  45. Indiveri G (2008) Neuromorphic VLSI models of selective attention: from single chip vision sensors to multi-chip systems. Sensors 8(9): 5352–5375CrossRefGoogle Scholar
  46. Indiveri G, Chicca E, Douglas R (2006) A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Trans Neural Netw 17(1): 211–221PubMedCrossRefGoogle Scholar
  47. Indiveri G, Chicca E, Douglas R (2009) Artificial cognitive systems: from VLSI networks of spiking neurons to neuromorphic cognition. Cogn Comput 1(2): 119–127CrossRefGoogle Scholar
  48. Jeltsch S (2010) Computing with transient states on a neuromorphic multi-chip environment. Diploma thesis (English), Ruprecht-Karls-Universität, Heidelberg, HD-KIP 10-54, http://www.kip.uni-heidelberg.de/Veroeffentlichungen/details.php?id=2095
  49. Jones E, Oliphant T, Peterson P (2001) SciPy: open source scientific tools for Python. http://www.scipy.org/
  50. Kaplan B, Brüderle D, Schemmel J, Meier K (2009) High-conductance states on a neuromorphic hardware system. In: Proceedings of the 2009 international joint conference on neural networks (IJCNN)Google Scholar
  51. Kremkow J, Kumar A, Rotter S, Aertsen A (2007) Emergence of population synchrony in a layered network of the cat visual cortex. Neurocomputing 70: 2069–2073CrossRefGoogle Scholar
  52. Kremkow J, Aertsen A, Kumar A (2010a) Gating of signal propagation in spiking neural networks by balanced and correlated excitation and inhibition. J Neurosci 30(47): 15760–15768PubMedCrossRefGoogle Scholar
  53. Kremkow J, Perrinet L, Masson G, Aertsen A (2010b) Functional consequences of correlated excitatory and inhibitory conductances. J Comput Neurosci 28(3): 579–594PubMedCrossRefGoogle Scholar
  54. Kuhn A, Aertsen A, Rotter S (2003) Higher-order statistics of input ensembles and the response of simple model neurons. Neural Comput 15(1): 67–101PubMedCrossRefGoogle Scholar
  55. Kumar A, Rotter S, Aertsen A (2008) Conditions for propagating synchronous spiking and asynchronous firing rates in a cortical network model. J Neurosci 28(20): 5268–5280PubMedCrossRefGoogle Scholar
  56. Kumar A, Rotter S, Aertsen A (2010) Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding. Nat Rev Neurosci 11(9): 615–627PubMedCrossRefGoogle Scholar
  57. Lande T, Ranjbar H, Ismail M, Berg Y (1996) An analog floating-gate memory in a standard digital technology. In: Proceedings of fifth international conference on microelectronics for neural networks, pp 271–276Google Scholar
  58. Langtangen HP (2008) Python scripting for computational science, 3rd edn. Springer, BerlinCrossRefGoogle Scholar
  59. Lewis MA, Etienne-Cummings R, Cohen AH, Hartmann M (2000) Toward biomorphic control using custom aVLSI chips. In: Proceedings of the international conference on robotics and automation. IEEE PressGoogle Scholar
  60. Lundqvist M, Rehn M, Djurfeldt M, Lansner A (2006) Attractor dynamics in a modular network of neocortex. Netw Comput Neural Syst 17(3): 253–276CrossRefGoogle Scholar
  61. Lundqvist M, Compte A, Lansner A (2010) Bistable, irregular firing and population oscillations in a modular attractor memory network. PLoS Comput Biol 6(6)Google Scholar
  62. Markram H, Toledo-Rodriguez M, Wang Y, Gupta A, Silberberg G, Wu C (2004) Interneurons of the neocortical inhibitory system. Nat Rev Neurosci 5(10): 793–807PubMedCrossRefGoogle Scholar
  63. Mead CA (1989) Analog VLSI and neural systems. Addison Wesley, ReadingGoogle Scholar
  64. Mead CA (1990) Neuromorphic electronic systems. Proc IEEE 78: 1629–1636CrossRefGoogle Scholar
  65. Mead CA, Mahowald MA (1988) A silicon model of early visual processing. Neural Netw 1(1): 91–97CrossRefGoogle Scholar
  66. Merolla PA, Boahen K (2006) Dynamic computation in a recurrent network of heterogeneous silicon neurons. In: Proceedings of the 2006 IEEE international symposium on circuits and systems (ISCAS)Google Scholar
  67. Millner S, Grübl A, Schemmel J, Meier K, Schwartz M-O (2010) A VLSI implementation of the adaptive exponential integrate-and-fire neuron model. In: Advances in neural information processing systems (NIPS), vol 23, pp 1642–1650Google Scholar
  68. Mitra S, Fusi S, Indiveri G (2009) Real-time classification of complex patterns using spike-based learning in neuromorphic VLSI. IEEE Trans Biomed Circuits Syst 3(1): 32–42CrossRefGoogle Scholar
  69. Morrison A, Mehring C, Geisel T, Aertsen A, Diesmann M (2005) Advancing the boundaries of high connectivity network simulation with distributed computing. Neural Comput 17(8): 1776–1801PubMedCrossRefGoogle Scholar
  70. Morrison A, Aertsen A, Diesmann M (2007) Spike-timing-dependent plasticity in balanced random networks. Neural Comput 19(6): 1437–1467PubMedCrossRefGoogle Scholar
  71. Morrison A, Diesmann M, Gerstner W (2008) Phenomenological models of synaptic plasticity based on spike timing. Biol Cybern 98(6): 459–478PubMedCrossRefGoogle Scholar
  72. Mountcastle VB (1997) The columnar organization of the neocortex. Brain 120(4): 701–722PubMedCrossRefGoogle Scholar
  73. Naud R, Marcille N, Clopath C, Gerstner W (2008) Firing patterns in the adaptive exponential integrate-and-fire model. Biol Cybern 99(4): 335–347PubMedCrossRefGoogle Scholar
  74. Netter T, Franceschini N (2002) A robotic aircraft that follows terrain using a neuromorphic eye. In: Conf. intelligent robots and system, pp 129–134Google Scholar
  75. NeuroTools (2008) Website. http://neuralensemble.org/trac/NeuroTools
  76. Norris M (2003) Gigabit ethernet technology and applications. Artech House, BostonGoogle Scholar
  77. Oliphant TE (2007) Python for scientific computing. IEEE Comput Sci Eng 9(3): 10–20Google Scholar
  78. Oster M, Whatley AM, Liu SC, Douglas RJ (2005) A hardware/software framework for real-time spiking systems. In: Proceedings of the 2005 international conference on artificial neural networks (ICANN)Google Scholar
  79. Pecevski DA, Natschläger T, Schuch KN (2009) PCSIM: a parallel simulation environment for neural circuits fully integrated with Python. Front Neuroinform 3:(11)Google Scholar
  80. Pfeiffer M, Nessler B, Douglas RJ, Maass W (2010) Reward-modulated hebbian learning of decision making. Neural Comput 22(6): 1399–1444PubMedCrossRefGoogle Scholar
  81. Philipp S, Schemmel J, Meier K (2009) A QoS network architecture to interconnect large-scale VLSI neural networks. In: Proceedings of the 2009 international joint conference on neural networks (IJCNN), pp 2525–2532Google Scholar
  82. Pospischil M, Toledo-Rodriguez M, Monier C, Piwkowska Z, Bal T, Frégnac Y, Markram H, Destexhe A (2008) Minimal hodgkin-huxley type models for different classes of cortical and thalamic neurons. Biol Cybern 99(4): 427–441PubMedCrossRefGoogle Scholar
  83. Renaud S, Tomas J, Bornat Y, Daouzli A, Saighi S (2007) Neuromimetic ICs with analog cores: an alternative for simulating spiking neural networks. In: Proceedings of the 2007 IEEE symposium on circuits and systems (ISCAS)Google Scholar
  84. Schemmel J, Meier K, Muller E (2004) A new VLSI model of neural microcircuits including spike time dependent plasticity. In: Proceedings of the 2004 international joint conference on neural networks (IJCNN), IEEE Press, pp 1711–1716Google Scholar
  85. Schemmel J, Grübl A, Meier K, Muller E (2006) Implementing synaptic plasticity in a VLSI spiking neural network model. In: Proceedings of the 2006 international joint conference on neural networks (IJCNN), IEEE PressGoogle Scholar
  86. Schemmel J, Brüderle D, Meier K, Ostendorf B (2007) Modeling synaptic plasticity within networks of highly accelerated I&F neurons. In: Proceedings of the 2007 IEEE international symposium on circuits and systems (ISCAS), IEEE Press, pp 3367–3370Google Scholar
  87. Schemmel J, Fieres J, Meier K (2008) Wafer-scale integration of analog neural networks. In: Proceedings of the 2008 international joint conference on neural networks (IJCNN)Google Scholar
  88. Schemmel J, Brüderle D, Grübl A, Hock M, Meier K, Millner S (2010) A wafer-scale neuromorphic hardware system for large-scale neural modeling. In: Proceedings of the 2010 IEEE international symposium on circuits and systems (ISCAS), pp 1947–1950Google Scholar
  89. Schilling M (2010) A highly efficient transport layer for the connection of neuromorphic hardware systems. Diploma thesis, Ruprecht-Karls-Universität, Heidelberg, HD-KIP-10-09, http://www.kip.uni-heidelberg.de/Veroeffentlichungen/details.php?id=2000
  90. Scholze S, Henker S, Partzsch J, Mayr C, Schüffny R (2010) Optimized queue based communication in VLSI using a weakly ordered binary heap. In: Proceedings of the 2010 international conference on mixed design of integrated circuits and systems (MIXDES)Google Scholar
  91. Serrano-Gotarredona R, Oster M, Lichtsteiner P, Linares-Barranco A, Paz-Vicente R, Gómez-Rodríguez F, Riis HK, Delbrück T, Liu SC, Zahnd S, Whatley AM, Douglas RJ, Häfliger P, Jimenez-Moreno G, Civit A, Serrano-Gotarredona T, Acosta-Jiménez A, Linares-Barranco B (2006) AER building blocks for multi-layer multi-chip neuromorphic vision systems. In: Weiss Y, Schölkopf B, Platt J (eds) Advances in neural information processing systems, vol 18. MIT Press, Cambridge, pp 1217–1224Google Scholar
  92. Sjöström PJ, Rancz EA, Roth A, Häusser M (2008) Dendritic excitability and synaptic plasticity. Physiol Rev 88(2): 769–840PubMedCrossRefGoogle Scholar
  93. Steriade M (2003) Neuronal substrates of sleep and epilepsy. Cambridge University Press, CambridgeGoogle Scholar
  94. Tao L, Shelley M, McLaughlin D, Shapley R (2004) An egalitarian network model for the emergence of simple and complex cells in visual cortex. PNAS 101: 366–371PubMedCrossRefGoogle Scholar
  95. van Rossum MCW, Bi GQ, Turrigiano G (2000) Stable hebbian learning from spike timing-dependent plasticity. J Neurosci 20: 8812–8821PubMedGoogle Scholar
  96. Vogels TP, Abbott LF (2005) Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 25(46): 10786–10795PubMedCrossRefGoogle Scholar
  97. Vogelstein RJ, Mallik U, Vogelstein JT, Cauwenberghs G (2007) Dynamically reconfigurable silicon array of spiking neuron with conductance-based synapses. IEEE Trans Neural Netw 18: 253–265PubMedCrossRefGoogle Scholar
  98. Vogginger B (2010) Testing the operation workflow of a neuromorphic hardware system with a functionally accurate model. Diploma thesis, Ruprecht-Karls-Universität, Heidelberg, HD-KIP-10-12, http://www.kip.uni-heidelberg.de/Veroeffentlichungen/details.php?id=2003
  99. Wendt K, Ehrlich M, Schüffny R (2008) A graph theoretical approach for a multistep mapping software for the FACETS project. In: Proceedings of the 2008 WSEAS international conference on computer engineering and applications (CEA), pp 189–194Google Scholar
  100. Wendt K, Ehrlich M, Schüffny R (2010) GMPath—a path language for navigation, information query and modification of data graphs. In: Proceedings of ANNIIP 2010, pp 31–42Google Scholar
  101. Zucker RS, Regehr WG (2002) Short-term synaptic plasticity. Annu Rev Physiol 64: 355–405PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Daniel Brüderle
    • 1
  • Mihai A. Petrovici
    • 1
  • Bernhard Vogginger
    • 1
  • Matthias Ehrlich
    • 6
  • Thomas Pfeil
    • 1
  • Sebastian Millner
    • 1
  • Andreas Grübl
    • 1
  • Karsten Wendt
    • 6
  • Eric Müller
    • 1
  • Marc-Olivier Schwartz
    • 1
  • Dan Husmann de Oliveira
    • 1
  • Sebastian Jeltsch
    • 1
  • Johannes Fieres
    • 1
  • Moritz Schilling
    • 1
    • 2
  • Paul Müller
    • 1
  • Oliver Breitwieser
    • 1
  • Venelin Petkov
    • 1
  • Lyle Muller
    • 3
  • Andrew P. Davison
    • 3
  • Pradeep Krishnamurthy
    • 8
  • Jens Kremkow
    • 7
  • Mikael Lundqvist
    • 8
  • Eilif Muller
    • 9
  • Johannes Partzsch
    • 6
  • Stefan Scholze
    • 6
  • Lukas Zühl
    • 6
  • Christian Mayr
    • 6
  • Alain Destexhe
    • 3
  • Markus Diesmann
    • 4
    • 5
  • Tobias C. Potjans
    • 10
    • 11
  • Anders Lansner
    • 8
  • René Schüffny
    • 6
  • Johannes Schemmel
    • 1
  • Karlheinz Meier
    • 1
  1. 1.Kirchhoff Institute for Physics, Ruprecht-Karls-Universität HeidelbergHeidelbergGermany
  2. 2.Robotics Innovation Center, DFKI BremenBremenGermany
  3. 3.Unité de Neuroscience, Information et Complexité, CNRSGif sur YvetteFrance
  4. 4.RIKEN Brain Science Institute and RIKEN Computational Science Research ProgramWako-shiJapan
  5. 5.Bernstein Center for Computational NeuroscienceUniversität FreiburgFreiburgGermany
  6. 6.Institute of Circuits and SystemsTechnische Universität DresdenDresdenGermany
  7. 7.Bernstein Center FreiburgUniversity of FreiburgFreiburgGermany
  8. 8.Computational Biology, KTH StockholmStockholmSweden
  9. 9.Brain Mind Institute, Ecoles Polytechniques Federales de LausanneLausanneSwitzerland
  10. 10.Institute of Neuroscience and Medicine (INM-6), Research Center JülichJülichGermany
  11. 11.RIKEN Computational Science Research ProgramWako-shiJapan

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