Research > Simulation

Simulation of neural networks

I developed a new simulator for spiking neural networks named Brian, with Dan Goodman (4,6,7). It is written in pure Python, which makes it very easy to use, and yet very efficient, thanks to vectorised algorithms (9). It is ideally suited for rapid model writing and for teaching (I use it for my course in computational neuroscience). We also developed a toolbox for Brian to fit spiking models to electrophysiological recordings (8,10), as well as an auditory toolbox (11). We are currently working on including GPU support in the Brian simulator.

I previously worked on event-driven algorithms (1,3). Although neural networks are essentially defined by standard differential equations, the discontinuities caused by spikes make the efficient simulation of spiking neural networks a non-trivial problem. I reviewed simulation algorithms in (2). I recently developed algorithms to generate sets of spike trains with prescribed rates and (non-instantaneous) pair-wise correlations (5), which are partially included in Brian.

I have also written a simple event-driven simulator in Scilab for networks without delays. Scilab is a free vector-based scientific software (resembling Matlab). The code is included in the supplementary code for the article "Exact simulation of integrate-and-fire models with synaptic conductances". Please note that this code is mostly for pedagogical purposes and is not meant to be efficient in any way.

Relevant publications (chronological order):

1. Brette, R. (2006). Exact simulation of integrate-and-fire models with synaptic conductances. Neural Computation 18(8): 2004-2027.
2. Brette, R. et al (2007). Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci 23(3):349-98.
3. Brette, R. (2007). Exact simulation of integrate-and-fire models with exponential currents. Neural Computation 19(10): 2604-2609.
4. Goodman D and R Brette (2008). Brian: a simulator for spiking neural networks in Python. Front Neuroinform 2:5. doi:10.3389/neuro.11.005.2008.
5. Brette, R. (2009). Generation of correlated spike trains. Neural Comput 21(1): 188–215.
6. Brette, R. and D. Goodman (2009). Brian: a simple and flexible simulator for spiking neural networks. The Neuromorphic Engineer, doi:10.2417/1200907.1659.
7. Goodman, D. and R. Brette (2009). The Brian simulator. Front Neurosci doi:10.3389/neuro.01.026.2009.
8. Rossant C, Goodman DF, Platkiewicz J and Brette R (2010). Automatic fitting of spiking neuron models to electrophysiological recordings. Front. Neuroinform. doi:10.3389/neuro.11.002.2010
9. Brette R and DF Goodman (2011). Vectorised algorithms for spiking neural network simulation, Neural Comput 23(6), 1503-1535.
10. Rossant C, Goodman DF, Fontaine B, Platkiewicz J, Magnusson AK and Brette R (2011). Fitting neuron models to spike trains. Front Neurosci. 5:9. doi: 10.3389/fnins.2011.00009.
11. Fontaine B, Goodman DFM, Benichoux F, Brette R (2011). Brian Hears: online auditory processing using vectorisation over channels. Front Neuroinf 5:9. doi: 10.3389/fninf.2011.00009.

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Book: The Handbook of Neural Activity Measurement