The Jython scripting interface in Nengo provides complete access to all of the underlying Java functionality. This provides complete flexibility, but requires users to manually create all of the standard components (Origins, Terminations, Projections, etc), resulting in fairly repetitive code.
To simplify the creation of Nengo models, we have developed the nef module as a wrapper around the most common functions needed for creating Nengo networks. As an example, the following code will create a new network with an input that feeds into ensemble A which feeds into ensemble B:
import nef net=nef.Network('Test Network') net.add_to_nengo() net.make_input('input',values=) net.make('A',neurons=100,dimensions=1) net.make('B',neurons=100,dimensions=1) net.connect('input','A') net.connect('A','B')
These scripts can be created with any text editor, saved with a .py extension, and run in Nengo by choosing File->Open from the menu or clicking on the blue Open icon in the upper-left. All of the examples in the demo directory have been written using this system.
The first thing you need to do is to create your new network object and tell it to appear in the Nengo user interface:
import nef net=nef.Network('My Network') net.add_to_nengo()
Now we can create ensembles in our network. You must specify the name of the ensemble, the number of neurons, and the number of dimensions:
net.make('A',neurons=100,dimensions=1) net.make('B',1000,2) net.make('C',50,10)
You can also specify the radius for the ensemble. The neural representation will be optimized to represent values inside a sphere of the specified radius. So, if you have a 2-dimensional ensemble and you want to be able to represent the value (10,-10), you should have a radius of around 15:
To create a simple input that has a constant value (which can then be controlled interactive mode interface), do the following:
The name can anything, and the values is an array of the required length. So, for a 5-dimensional input, you can do:
You can also use values other than 0:
To have components be useful, they have to be connected to each other. To assist this process, the nef.Network.connect() function will create the necessary Origins and/or Terminations as well as the Projection:
net.make('A',100,1) net.make('B',100,1) net.connect('A','B')
You can also specify a transformation matrix (to allow for the computation of any linear function) and a post-synaptic time constant:
net.make('A',100,2) net.make('B',100,3) net.connect('A','B',transform=[[0,0.5],[1,0],[0,0.5]],pstc=0.03)
To compute nonlinear functions, you can specify a function to compute and an origin will automatically be created:
net.make('A',100,1) net.make('B',100,1) def square(x): return x*x net.connect('A','B',func=square)
This also works for highly complex functions:
net.make('A',100,5) net.make('B',100,1) import math def strange(x): if x<0.4: return 0.3 elif x*x<0.3: return math.sin(x) else: return x net.connect('A','B',func=strange)
Nengo will automatically solve for the decoders and connection weights needed to approximate these highly complex functions.