Publications about, relating to or using PyNN

  • Schmuker, Michael, Pfeil, Thomas and Nawrot, Martin Paul (2014) A neuromorphic network for generic multivariate data classification Proceedings of the National Academy of Sciences 111: 2081-2086. doi: 10.1073/pnas.1303053111 [link]

  • Kaplan, BA, Khoei, MA, Lansner, A, & Perrinet, LU (2014) Signature of an anticipatory response in area VI as modeled by a probabilistic model and a spiking neural network. In: Neural Networks (IJCNN), 2014 International Joint Conference on (pp. 3205-3212). Beijing, China. IEEE. doi: 10.1109/IJCNN.2014.6889847 [link]

  • Djurfeldt M., Davison A.P. and Eppler J.M. (2014) Efficient generation of connectivity in neuronal networks from simulator-independent descriptions. Frontiers in Neuroinformatics 8:43: doi: 10.3389/fninf.2014.00043 [link]

  • Antolík J. and Davison A.P. (2013) Integrated workflows for spiking neuronal network simulations. Frontiers in Neuroinformatics 7:34: 10.3389/fninf.2013.00034 [link]

  • Pfeil, Thomas, Grübl, Andreas, Jeltsch, Sebastian, Müller, Eric, Müller, Paul, Petrovici, Mihai A., Schmuker, Michael, Brüderle, Daniel, Schemmel, Johannes and Meier, Karlheinz (2013) Six networks on a universal neuromorphic computing substrate. Frontiers in Neuroscience 7:11 doi: 10.3389/fnins.2013.00011 [link]

  • Kaplan BA, Lansner A, Masson GS and Perrinet LU (2013) Anisotropic connectivity implements motion-based prediction in a spiking neural network. Front. Comput. Neurosci. 7:112. doi: 10.3389/fncom.2013.00112 `[link] <>`_

  • Brüderle D., Petrovici M.A., Vogginger B., Ehrlich M., Pfeil T., Millner S., Grübl A., Wendt K., Müller E., Schwartz M.O., Husmann de Oliveira D., Jeltsch S., Fieres J., Schilling M., Müller P., Breitwieser O., Petkov V., Muller L., Davison A.P., Krishnamurthy P., Kremkow J., Lundqvist M., Muller E., Partzsch J., Scholze S., Zühl L., Mayr C., Destexhe A., Diesmann M., Potjans T.C., Lansner A., Schüffny R., Schemmel J., Meier K. (2011) A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems. Biological Cybernetics 104: 263-296. doi: 10.1007/s00422-011-0435-9 [link]

  • Galluppi, Francesco, Rast, Alexander, Davies, Sergio and Furber, Steve (2010) A general-purpose model translation system for a universal neural chip. Neural Information Processing. Theory and Algorithms; Lecture Notes in Computer Science vol 6443, pp58-65 [link]

  • J. Nageswaran, N. Dutt, J. L. Krichmar, A. Nicolau, A. V. Veidenbaum (2009) A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors. Neural Networks 22:5-6, doi:10.1016/j.neunet.2009.06.028. [link]

  • Davison AP, Hines M and Muller E (2009) Trends in programming languages for neuroscience simulations. Front. Neurosci. doi:10.3389/neuro.01.036.2009. [link]

  • Davison AP, Brüderle D, Eppler J, Kremkow J, Muller E, Pecevski D, Perrinet L and Yger P (2009) PyNN: a common interface for neuronal network simulators. Front. Neuroinform. 2:11. doi:10.3389/neuro.11.011.2008. [link]

  • Brüderle D, Muller E, Davison A, Muller E, Schemmel J and Meier K (2009) Establishing a novel modeling tool: a python-based interface for a neuromorphic hardware system. Front. Neuroinform. 3:17. doi:10.3389/neuro.11.017.2009. [link]

  • Bednar JA (2009) Topographica: building and analyzing map-level simulations from Python, C/C++, MATLAB, NEST, or NEURON components. Front. Neuroinform. 3:8. doi:10.3389/neuro.11.008.2009. [link]

  • Goodman D and Brette R (2008) Brian: a simulator for spiking neural networks in Python. Front. Neuroinform. 2:5. doi:10.3389/neuro.11.005.2008. [link]

  • Pecevski D, Natschläger T and Schuch K (2009) PCSIM: a parallel simulation environment for neural circuits fully integrated with Python. Front. Neuroinform. 3:11. doi:10.3389/neuro.11.011.2009. [link]

  • Ray S and Bhalla US (2008) PyMOOSE: interoperable scripting in Python for MOOSE. Front. Neuroinform. 2:6. doi:10.3389/neuro.11.006.2008. [link]

  • Sharon Crook, R Angus Silver and Padraig Gleeson (2009) Describing and exchanging models of neurons and neuronal networks with NeuroML. BMC Neuroscience, 10(Suppl 1):L1doi:10.1186/1471-2202-10-S1-L1. [link]

  • D. Brüderle, A. Grübl, K. Meier, E. Muller and J. Schemmel (2007) A Software Framework for Tuning the Dynamics of Neuromorphic Silicon Towards Biology. LNCS 4507. doi:10.1007/978-3-540-73007-1. [link]

  • B. Kaplan, D. Brüderle, J. Schemmel and K. Meier (2009) High-Conductance States on a Neuromorphic Hardware System. Proceedings of IJCNN 2009. [link]

  • D. Brüderle (2009) Neuroscientific Modeling with a Mixed-Signal VLSI Hardware System. Doctoral Dissertation, Kirchhoff-Institute for Physics, University of Heidelberg. [link]

  • A. Davison, P. Yger, J. Kremkow, L. Perrinet and E. Muller (2007) PyNN: towards a universal neural simulator API in Python. BMC Neuroscience 2007, 8(Suppl 2):P2. doi:10.1186/1471-2202-8-S2-P2. [link]

  • E. Muller, A. P. Davison, T. Brizzi, D. Bruederle, M. J. Eppler, J. Kremkow, D. Pecevski, L. Perrinet, M. Schmuker and P. Yger (2009) NeuralEnsemble.Org: Unifying neural simulators in Python to ease the model complexity bottleneck. Frontiers in Neuroinformatics Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.104. [link]