Brian: a simulator for spiking neural networks in python. ”Brian” is a new simulator for spiking neural networks, written in Python (http://brian. It is an intuitive and highly flexible tool for rapidly developing new models, especially networks of single-compartment neurons. In addition to using standard types of neuron models, users can define models by writing arbitrary differential equations in ordinary mathematical notation. Python scientific libraries can also be used for defining models and analysing data. Vectorisation techniques allow efficient simulations despite the overheads of an interpreted language. Brian will be especially valuable for working on non-standard neuron models not easily covered by existing software, and as an alternative to using Matlab or C for simulations. With its easy and intuitive syntax, Brian is also very well suited for teaching computational neuroscience.

References in zbMATH (referenced in 29 articles )

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  1. Górski, Tomasz; Depannemaecker, Damien; Destexhe, Alain: Conductance-based adaptive exponential integrate-and-fire model (2021)
  2. Toğaçar, Mesut; Cömert, Zafer; Ergen, Burhan: Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks (2021)
  3. Yin, Yonghua: Random neural network methods and deep learning (2021)
  4. Zixuan Zhao, Nathan Wycoff, Neil Getty, Rick Stevens, Fangfang Xia: Neko: a Library for Exploring Neuromorphic Learning Rules (2021) arXiv
  5. Wang, Ziqi; Dai, Wei; McLaughlin, David W.: Ring models of binocular rivalry and fusion (2020)
  6. Andalibi, Vafa; Hokkanen, Henri; Vanni, Simo: Controlling complexity of cerebral cortex simulations. I: CxSystem, a flexible cortical simulation framework (2019)
  7. Heitmann S, Aburn M, Breakspear M: The Brain Dynamics Toolbox for Matlab (2018) not zbMATH
  8. Higgins, Irina; Stringer, Simon; Schnupp, Jan: A computational account of the role of cochlear nucleus and inferior colliculus in stabilizing auditory nerve firing for auditory category learning (2018)
  9. van Pottelbergh, Tomas; Drion, Guillaume; Sepulchre, Rodolphe: Robust modulation of integrate-and-fire models (2018)
  10. Zerlaut, Yann; Chemla, Sandrine; Chavane, Frederic; Destexhe, Alain: Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons (2018)
  11. Tekin, Ramazan; Tagluk, Mehmet Emin: Effects of small-world rewiring probability and noisy synaptic conductivity on slow waves: cortical network (2017)
  12. Bellec, Guillaume; Galtier, Mathieu; Brette, Romain; Yger, Pierre: Slow feature analysis with spiking neurons and its application to audio stimuli (2016)
  13. 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)
  14. Ferguson, K. A.; Njap, F.; Nicola, W.; Skinner, Frances K.; Campbell, S. A.: Examining the limits of cellular adaptation bursting mechanisms in biologically-based excitatory networks of the hippocampus (2015)
  15. Yi, Guosheng; Wang, Jiang; Tsang, Kai-Ming; Wei, Xile; Deng, Bin; Han, Chunxiao: Spike-frequency adaptation of a two-compartment neuron modulated by extracellular electric fields (2015)
  16. Bonaiuto, James; Arbib, Michael A.: Modeling the BOLD correlates of competitive neural dynamics (2014) ioport
  17. D’Haene, Michiel; Hermans, Michiel; Schrauwen, Benjamin: Toward unified hybrid simulation techniques for spiking neural networks (2014)
  18. Graben, Peter Beim; Rodrigues, Serafim: On the electrodynamics of neural networks (2014)
  19. Gürcan, Önder: Effective connectivity at synaptic level in humans: a review and future prospects (2014)
  20. Hutt, Axel; Buhry, Laure: Study of GABAergic extra-synaptic tonic inhibition in single neurons and neural populations by traversing neural scales: application to propofol-induced anaesthesia (2014)

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