Transcript What is Brian not for?

http://brian.di.ens.fr
Dan Goodman & Romain Brette
Ecole Normale Supérieure
Projet Odyssée
[email protected]
[email protected]
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
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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
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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
http://neuralensemble.org/cookbook
Piquant units package
• Brian’s units module released as a stand-alone package
Piquant, available at
http://sourceforge.net/projects/piquant/
• 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
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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?