**Assignment 4.**
Many biological sytems and important engineering systems can not be
expressed as feed-forward networks or simple Elman or Jordan nets. The
Tlearn simulator is not designed for these "structured" connectionist nets,
but will allow us to experiment with some simple cases. For this exercise,
all of the weights will be fixed at the time of specification; you will be
designing networks with the required properties. Tlearn 's method for specifying
fixed links is a bit clumsy, but adequate and is described in the manual. It turns
out that you will need to use one trivial training run to get Tlearn to initialize
your weights properly. Then you can test the network's behavior.
**a)**
The first task is to build a simple winner-take-all (WTA) network using linear
Tlearn nodes.

**b)**
The other part of this assignment is to hand-build a network that captures the
Necker Cube behavior from the first lecture. Recall that the Necker figure is
a wire-frame cube with 8 vertices. Your net should have two output units
corresponding to the two stable states. For each vertex, you should have a WTA pair
of units. There should be positive connections between units in each that are
part of the same view. You should be able to get by with just two input
units to bias the network. Again because activations are preserved over test runs,
a small number of test cases should suffice.
It is worth some thought on your numbering scheme to fit best with Tlearn's
layout and evaluation schemes.

**c)**
Why does the Necker network converge faster than the WTA in part (a)?

*This assignment is due in class on September 25, or by earlier*

*electronic submission.*