Added maxint to numpy.py

- This is used in builder instead of the old helper `random_maxint`
- Also edited `rmse` a bit; added docs, kwargs
Made builder pluggable

This means that modellers can create their own neuron types
or NengoObjects or learning rules, etc., and inject them into
the reference simulator. Granted, this requires some pretty
advanced knowledge of the entire codebase, but it's important
that something like this is at least possible.

More concretely, the main change is to move a few things from
Builder into Model, and then restructure Builder such that
build functions register themselves with Builder,
and the only function that is ever really called is
Builder.build, which finds the correct builder function and
calls it with the passed arguments.
This makes it possible to override build functions
and make objects with associated build functions.
It also provides a consistent interface through which
to build an arbitrary Nengo object; now, regardless of the object,
calling Builder.build(object, model=model) will insert
the appropriate build artifacts into the passed model
(if a build method has been associated with that object type).
This opens up the possibility that the user can do the build
manually, and pass a built Model to the simulator.
This is definitely not the normal use case, but we do this
in `test_simulator`, so it's now possible to pass in a model
instead of a network to the simulator (but the network must
be explicitly passed None to do so).

Other changes
- Cleaned up docstrings in builder.py
- Moved `assert_named_signals` into Signal and added test
Added rectified_linear example

This is an advanced example of how you can define a new neuron type
within a user script, if you want to do that kind of thing.

Mostly, it's a justification for the pluggable builder changes in
the previous commit. I don't believe that you could do this example
before the previous commit without doing some pretty hacky stuff.
Renamed filter_coefs to decay_coef

Trying to make the builder a bit more understandable;
o_coef and n_coef had no meaning to me, so perhaps this helps.

Addresses #124 from 7 months ago! Nice turnaround, us.
Product network for multiplying vectors
Basal Ganglia use nnls_L2nz and output filter
Updated CircularConvolution to use Product

Also better kwarg error handling
Updated EnsembleArray to use slice syntax

Also changed passthrough optimization to use transform_full
Added nengo.utils.testing.Timer

This is for quick time profiling of blocks of code.
We use this in a few places in unit tests.
Added tests for utils.decorators.memoize

Currently these are commented out.
Improved memoize decorator

Used the existing decorator decorator to make memoize
work in more situations than before.
Introspection on memoized functions will now work as expected.
The previously commented out tests now pass.
Update utils.decorators docstring
Simplified CircularConvolution

We're no longer bypassing product.A and product.B.
This involved some rearranging of CircularConvolution
to attempt to make it more understandable.

Shoutout to @hunse for fixing the circularconv transforms test
and explaining how the network works!
EnsembleArray kwarg dimensions -> ens_dimensions

To help differentiate it from the `dimensions` property.
Error if dimensions passed to EnsembleArray

- We want to make these look less like ensembles,
  so we raise an error that tells the user to set either
  n_ensembles or ens_dimensions
- Added a tiny test to make sure dimensions can't be passed
Added docstring for `CircularConvolution`.
Simplified test_memoize

Now we just test for overall cache misses instead of checking
each call individually.

Also now making sure it works with @property. Only works
if @property is applied first.
Added system to specify default parameters

This is done at Network level
Bugfix with hierarchical interactions
Added separate config context stack