Dan Goodman & Romain Brette Ecole Normale Supérieure Projet Odyssée

Brian: a pure Python simulator What is Brian for? Quick model coding for every day use Easy to learn and intuitive Equations-oriented: flexible What is Brian not for? Very large-scale network models (distributed) Very detailed biophysical models

Brian in action PiPe Ce Ci P

Flexibility: adaptive threshold Threshold increases when spike arrives and decays Implemented as DE and user-defined reset and threshold functions

Performance Python is interpreted, but Brian uses NumPy for fast vector operations Python overheads add up to O(1/dt) For large N, performance improves Faster than Matlab, not much slower than C++ (1.7x)

Interoperability Python Brian’s data types PyNN NeuralEnsemble Computational neuroscience cookbook Piquant units package

Python and data types Brian is pure Python, all of its output can be used with any Python package, e.g. –NumPy (numerical) –SciPy (scientific) –PyLab (plotting) Brian’s output data types: –Quantities with physical dimensions: Quantity class derived from float, so easily compatible –Arrays with units: qarray class derived from numpy.ndarray, so easily compatible –Will talk more about Quantity and qarray later –Spike trains are lists of tuples (i,t) indicating that neuron i fired at time t

PyNN PyNN.brian in development Brian’s data structures easily compatible with PyNN, e.g. spike trains are exactly the same Through PyNN get support for many other things, e.g. –NeuroML –FacetsML

NeuralEnsemble and Cookbook NeuralEnsemble projects are in Python, so compatibility with Brian is good Brian data can be analysed with NeuroTools Computational neuroscience cookbook will facilitate code sharing and interoperability, see it at

Piquant units package Brian’s units module released as a stand-alone package Piquant, available at Idea is to have a standard package for single quantities and arrays with units for interoperability Piquant works by –Building on NumPy data types –Quantity derived from float –qarray derived from numpy.ndarray Current implementation –Single quantities good and reasonably stable –qarray slow and slightly patchy implementation, needs to be rewritten Looking for volunteers to help (get in touch!)

Piquant ideas (time allowing) Quantity –Deriving from float seems like a good idea, probably won’t change –Standard SI names: volt, amp, etc. –Standard SI prefixes: mvolt, namp, etc. –Some short names: mV, nA, etc. qarray –Current implementation has arrays with homogeneous units, or inhomogeneous Homogeneous fairly fast, just store one unit Inhomogeneous uses ndarray with dtype=object, very slow –Could use NumPy’s broadcasting rule for more flexible homogeneity Underlying array of floats X Array of units U that should be broadcastable to X, e.g. one unit for each row of X, or one unit for each column of X Performance? Compatibility with NumPy and SciPy?