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 22 articles )

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  1. Zixuan Zhao, Nathan Wycoff, Neil Getty, Rick Stevens, Fangfang Xia: Neko: a Library for Exploring Neuromorphic Learning Rules (2021) arXiv
  2. Wang, Ziqi; Dai, Wei; McLaughlin, David W.: Ring models of binocular rivalry and fusion (2020)
  3. Heitmann S, Aburn M, Breakspear M: The Brain Dynamics Toolbox for Matlab (2018) not zbMATH
  4. 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)
  5. Tekin, Ramazan; Tagluk, Mehmet Emin: Effects of small-world rewiring probability and noisy synaptic conductivity on slow waves: cortical network (2017)
  6. Bellec, Guillaume; Galtier, Mathieu; Brette, Romain; Yger, Pierre: Slow feature analysis with spiking neurons and its application to audio stimuli (2016)
  7. 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)
  8. 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)
  9. 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)
  10. Bonaiuto, James; Arbib, Michael A.: Modeling the BOLD correlates of competitive neural dynamics (2014) ioport
  11. D’Haene, Michiel; Hermans, Michiel; Schrauwen, Benjamin: Toward unified hybrid simulation techniques for spiking neural networks (2014)
  12. Gürcan, Önder: Effective connectivity at synaptic level in humans: a review and future prospects (2014)
  13. 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)
  14. Ingber, Lester; Pappalepore, Marco; Stesiak, Ronald R.: Electroencephalographic field influence on calcium momentum waves (2014)
  15. Zachariou, Margarita; Thul, Rüdiger: Cannabinoid-mediated short-term plasticity in hippocampus (2014)
  16. Adams, Samantha V.; Wennekers, Thomas; Denham, Sue; Culverhouse, Phil F.: Adaptive training of cortical feature maps for a robot sensorimotor controller (2013) ioport
  17. Cassidy, Andrew S.; Georgiou, Julius; Andreou, Andreas G.: Design of silicon brains in the nano-CMOS era: spiking neurons, learning synapses and neural architecture optimization (2013) ioport
  18. Yamazaki, Tadashi; Igarashi, Jun: Realtime cerebellum: a large-scale spiking network model of the cerebellum that runs in realtime using a graphics processing unit (2013) ioport
  19. 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
  20. 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

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