Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

PyNN: A Python API for Neural Network Modeling

  • Andrew P. Davison
Living reference work entry

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DOI: https://doi.org/10.1007/978-1-4614-7320-6_261-5

Definition

PyNN is an application programming interface (API) for describing and simulating neuronal network models in the Python programming language. Numerical solution of the model equations is performed by a “backend” simulator, which as of PyNN version 0.7 can be any of NEURON (Hines and Carnevale 1997), NEST (Gewaltig and Diesmann 2007), Brian (Goodman and Brette 2008), or PCSIM (Pecevski et al. 2009). Alternatively, the backend can be a neuromorphic hardware system (Bruederle et al. 2009; Galluppi et al. 2010). PyNN thus provides a simulator-independent method for describing spiking neuronal network models. Data generated by the backend simulator is reformatted by PyNN into a standard format, and thus PyNN can also be used as a base platform on which to build simulator-independent data analysis and visualization tools.

Detailed Description

The PyNN API allows the construction and simulation of neuronal network models in a simulator-independent way, i.e., the same Python script...

Keywords

Application Programming Interface Spike Neural Network Neural Network Simulator Neuronal Network Model Python Interface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. Bruederle D, Muller 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 Neuroinformatics 3:17CrossRefGoogle Scholar
  2. Davison AP, Hines M, Muller E (2009) Trends in programming languages for neuroscience simulations. Front Neurosci 3:3CrossRefGoogle Scholar
  3. Galluppi F, Rast A, Davies S, Furber S (2010) A general-purpose model translation system for a universal neural chip. In: Wong KW, Mendis BSU, Bouzerdoum A (eds) Neural information processing: theory and algorithms. Springer, Berlin/Heidelberg, pp 58–65Google Scholar
  4. Gewaltig MO, Diesmann M (2007) NEST (NEural Simulation Tool). Scholarpedia 2:1430CrossRefGoogle Scholar
  5. Gleeson P, Crook S, Cannon RC, Hines ML, Billings GO, Farinella M, Morse TM, Davison AP, Ray S, Bhalla US, Barnes SR, Dimitrova YD, Silver RA (2010) NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Comput Biol 6:e1000815PubMedCentralPubMedCrossRefGoogle Scholar
  6. Goodman D, Brette R (2008) Brian: a simulator for spiking neural networks in Python. Front Neuroinformatics 2:5Google Scholar
  7. Hines ML, Carnevale NT (1997) The NEURON simulation environment. Neural Comput 9:1179–1209PubMedCrossRefGoogle Scholar
  8. Pecevski D, Natschläger T, Schuch K (2009) PCSIM: a parallel simulation environment for neural circuits fully integrated with Python. Front Neuroinformatics 3:11PubMedCentralCrossRefGoogle Scholar

Further Reading

  1. Davison AP, Brüderle D, Eppler JM, Kremkow J, Muller E, Pecevski DA, Perrinet L, Yger P (2009) PyNN: a common interface for neuronal network simulators. Front Neuroinformatics 2:11PubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Unité de Neurosciences, Information et Complexité (UNIC), Institut de Neurobiologie Alfred FessardCentre national de la recherche scientifique (CNRS)Gif sur YvetteFrance