Pynn: A common interface for neuronal network simulators. PyNN (pronounced ’pine’) is a simulator-independent language for building neuronal network models. In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON, NEST, PCSIM and Brian). The PyNN API aims to support modelling at a high-level of abstraction (populations of neurons, layers, columns and the connections between them) while still allowing access to the details of individual neurons and synapses when required. PyNN provides a library of standard neuron, synapse and synaptic plasticity models, which have been verified to work the same on the different supported simulators. PyNN also provides a set of commonly-used connectivity algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes it easy to provide your own connectivity in a simulator-independent way, either using the Connection Set Algebra (Djurfeldt, 2010) or by writing your own Python code.

References in zbMATH (referenced in 18 articles )

Showing results 1 to 18 of 18.
Sorted by year (citations)

  1. Yin, Yonghua: Random neural network methods and deep learning (2021)
  2. Zixuan Zhao, Nathan Wycoff, Neil Getty, Rick Stevens, Fangfang Xia: Neko: a Library for Exploring Neuromorphic Learning Rules (2021) arXiv
  3. Huyck, Christian Robert; Vergani, Alberto Arturo: Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons (2020)
  4. Andalibi, Vafa; Hokkanen, Henri; Vanni, Simo: Controlling complexity of cerebral cortex simulations. I: CxSystem, a flexible cortical simulation framework (2019)
  5. Diamond, Alan; Schmuker, Michael; Nowotny, Thomas: An unsupervised neuromorphic clustering algorithm (2019)
  6. Hananel Hazan, Daniel J. Saunders, Hassaan Khan, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma: BindsNET: A machine learning-oriented spiking neural networks library in Python (2018) arXiv
  7. Kasabov, Nikola; Scott, Nathan Matthew; Tu, Enmei; Marks, Stefan; Sengupta, Neelava; Capecci, Elisa; Othman, Muhaini; Doborjeh, Maryam Gholami; Murli, Norhanifah; Hartono, Reggio; Espinosa-Ramos, Josafath Israel; Zhou, Lei; Alvi, Fahad Bashir; Wang, Grace; Taylor, Denise; Feigin, Valery; Gulyaev, Sergei; Mahmoud, Mahmoud; Hou, Zeng-Guang; Yang, Jie: Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: design methodology and selected applications (2016)
  8. Lytton, William W.; Seidenstein, Alexandra H.; Dura-Bernal, Salvador; McDougal, Robert A.; Schürmann, Felix; Hines, Michael L.: Simulation neurotechnologies for advancing brain research: parallelizing large networks in NEURON (2016)
  9. Le Mouel, Charlotte; Harris, Kenneth D.; Yger, Pierre: Supervised learning with decision margins in pools of spiking neurons (2014)
  10. Brink, S.; Nease, S.; Hasler, P.: Computing with networks of spiking neurons on a biophysically motivated floating-gate based neuromorphic integrated circuit (2013) ioport
  11. Bray, Laurence C. Jayet; Anumandla, Sridhar R.; Thibeault, Corey M.; Hoang, Roger V.; Goodman, Philip H.; Dascalu, Sergiu M.; Bryant, Bobby D.; Jr., Frederick C. Harris: Real-time human-robot interaction underlying neurorobotic trust and intent recognition (2012) ioport
  12. Patterson, Cameron; Garside, Jim; Painkras, Eustace; Temple, Steve; Plana, Luis A.; Navaridas, Javier; Sharp, Thomas; Furber, Steve: Scalable communications for a million-core neural processing architecture (2012) ioport
  13. Verstraeten, David; Schrauwen, Benjamin; Dieleman, Sander; Brakel, Philemon; Buteneers, Pieter; Pecevski, Dejan: Oger: modular learning architectures for large-scale sequential processing (2012) ioport
  14. Brüderle, Daniel; Petrovici, Mihai A.; Vogginger, Bernhard; Ehrlich, Matthias; Pfeil, Thomas: A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems (2011) ioport
  15. Neftci, Emre; Chicca, Elisabetta; Indiveri, Giacomo; Douglas, Rodney: A systematic method for configuring VLSI networks of spiking neurons (2011)
  16. Rast, Alexander; Galluppi, Francesco; Davies, Sergio; Plana, Luis; Patterson, Cameron; Sharp, Thomas; Lester, David; Furber, Steve: Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware (2011) ioport
  17. Kremkow, Jens; Perrinet, Laurent U.; Masson, Guillaume S.; Aertsen, Ad: Functional consequences of correlated excitatory and inhibitory conductances in cortical networks (2010) ioport
  18. Moorkanikara Nageswaran, Jayram; Dutt, Nikil; Krichmar, Jeffrey L.; Nicolau, Alex; Veidenbaum, Alexander V.: A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors (2009) ioport