Brian: a simulator for spiking neural networks in Python
Top Cited Papers
Open Access
- 1 January 2008
- journal article
- research article
- Published by Frontiers Media SA in Frontiers in Neuroscience
- Vol. 2, 350
- https://doi.org/10.3389/neuro.11.005.2008
Abstract
“Brian” is a new simulator for spiking neural networks, written in Python (http://brain.di.ens.fr ). 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.Keywords
This publication has 9 references indexed in Scilit:
- Phenomenological models of synaptic plasticity based on spike timingBiological Cybernetics, 2008
- Simulation of networks of spiking neurons: A review of tools and strategiesJournal of Computational Neuroscience, 2007
- Interoperability of Neuroscience Modeling Software: Current Status and Future DirectionsNeuroinformatics, 2007
- Exact Subthreshold Integration with Continuous Spike Times in Discrete-Time Neural Network SimulationsNeural Computation, 2007
- Signal Propagation and Logic Gating in Networks of Integrate-and-Fire NeuronsJournal of Neuroscience, 2005
- ModelDB: A Database to Support Computational NeuroscienceJournal of Computational Neuroscience, 2004
- Towards NeuroML: Model Description Methods for Collaborative Modelling in NeurosciencePhilosophical Transactions Of The Royal Society B-Biological Sciences, 2001
- Competitive Hebbian learning through spike-timing-dependent synaptic plasticityNature Neuroscience, 2000
- The neural code between neocortical pyramidal neurons depends on neurotransmitter release probabilityProceedings of the National Academy of Sciences, 1997