Pynn

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 8 articles )

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  1. Brink, S.; Nease, S.; Hasler, P.: Computing with networks of spiking neurons on a biophysically motivated floating-gate based neuromorphic integrated circuit (2013)
  2. 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)
  3. 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)
  4. Verstraeten, David; Schrauwen, Benjamin; Dieleman, Sander; Brakel, Philemon; Buteneers, Pieter; Pecevski, Dejan: Oger: modular learning architectures for large-scale sequential processing (2012)
  5. 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)
  6. Neftci, Emre; Chicca, Elisabetta; Indiveri, Giacomo; Douglas, Rodney: A systematic method for configuring VLSI networks of spiking neurons (2011)
  7. 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)
  8. Kremkow, Jens; Perrinet, Laurent U.; Masson, Guillaume S.; Aertsen, Ad: Functional consequences of correlated excitatory and inhibitory conductances in cortical networks (2010)