Nengo models are composed of Nodes. Each Node has Origins (outputs) and Terminations (inputs), allowing them to be connected up to perform operations. Nengo has built-in Node types for Neurons, Ensembles (groups of Neurons), Networks, Arrays, and so on. However, when creating a complex model, we may want to create our own Node type. This might be to provide custom input or output from a model, or it can be used to have a non-neural component within a larger model.
Technically, the only requirement for being a Node is that an object supports the ca.nengo.model.Node interface. However, for the mast majority of cases, it is easier to make use of the nef.SimpleNode wrapper class.
You create a nef.SimpleNode by subclassing, defining functions to be called for whatever Origins and/or Terminations you want for this object. The functions you define will be called once every time step (usually 0.001 seconds). These functions can contain arbitrary code, allowing you to implement anything you want to. For example, the following code creates a Node that outputs a sine wave:
import nef import math net=nef.Network('Sine Wave') # define the SimpleNode class SineWave(nef.SimpleNode): def origin_wave(self): return [math.sin(self.t)] wave=net.add(SineWave('wave')) net.make('neurons',100,1) # connect the SimpleNode to the group of neurons net.connect(wave.getOrigin('wave'),'neurons') net.add_to_nengo()
You can create as many outputs as you want from a SimpleNode, as long as each one has a distinct name. Each origin consists of a single function that will get called once per time-step and must return an array of floats.
When defining this function, it is often useful to know the current simulation time. This can be accessed as self.t, and is the time (in seconds) of the beginning of the current time-step (the end of the current time step is self.t_end):
class ManyOrigins(nef.SimpleNode): # an origin that is 0 for t<0.5 and 1 for t>=0.5 def origin_step(self): if self.t<0.5: return  else: return  # a triangle wave with period of 1.0 seconds def origin_triangle(self): x=self.t%1.0 if x<0.5: return [x*2] else: return [2.0-x*2] # a sine wave and a cosine with frequency 10 Hz def origin_circle(self): theta=self.t*(2*math.pi)*10 return [math.sin(theta),math.cos(theta)]
When connecting a SimpleNode to other nodes, we need to specify which origin we are connecting. The name of the origin is determined by the function definition, of the form origin_<name>:
net.make('A',100,1) net.make('B',200,2) many=net.add(ManyOrigins('many')) net.connect(many.getOrigin('triangle'),'A') net.connect(many.getOrigin('circle'),'B')
To provide input to a SimpleNode, we define terminations. These are done in a similar manner as origins, but these functions take an input value (usually denoted x), which is an array of floats containing the input.
When definining the termination, we also have to define the number of dimensions expected. We do this by setting the dimensions parameter (which defaults to 1). We can also specify the post-synaptic time constant at this termination by setting the pstc parameter (default is None).
For example, the following object takes a 5-dimensional input vector and outputs the largest of the received values:
class Largest(nef.SimpleNode): def init(self): self.largest=0 def termination_values(self,x,dimensions=5,pstc=0.01): self.largest=max(x) def origin_largest(self): return [self.largest] net=nef.Network('largest') net.make_input('input',*5) largest=net.add(Largest('largest')) net.connect('input',largest.getTermination('values'))
When making a component like this, make sure to define an initial value for largest (or whatever internal parameter is being used to map inputs to outputs) inside the init(self) function. This function will be called before the origins are evaluated so that there is a valid self.largest return value.
You can also define a function that will be called every time step, but which is not tied to a particular Origin or Termination. This function is called tick. Here is a simple example where this function simply prints the current time:
class Time(nef.SimpleNode): def tick(self): print 'The current time in the simulation is:',self.t
As a more complex example, here is a tick function used to save spike raster information to a text file while the simulation runs:
class SpikeSaver(nef.SimpleNode): def tick(self): f=file('data.csv','a+') data=A.getOrigin('AXON').getValues().getValues() f.write('%1.3f,%s\n'%(self.t,list(data))) f.close() net=nef.Network('Spike Saver example') A=net.make('A',50,1) saver=net.add(SpikeSaver('saver'))