The connections in a B-type neural network can be trained. The following quote and figure are taken from the article Turing’s Neural Networks of 1948, by Jack Copeland and Diane Proudfoot:

Turing introduced a type of neural network that he called a ‘B-type unorganised machine’, consisting of artificial neurons, depicted below as circles, and connection-modifiers, depicted as boxes. A B-type machine may contain any number of neurons connected together in any pattern, but subject always to the restriction that each neuron-to-neuron connection passes through a connection-modifier.

A connection-modifier has two training fibres (coloured green and red in the diagram). Applying a pulse to the green training fibre sets the box to pass its input–either 0 or 1–straight out again. This is pass mode. In pass mode, the box’s output is identical to its input. The effect of a pulse on the red fibre is to place the modifier in interrupt mode. In this mode, the output of the box is always 1, no matter what its input. While it is in interrupt mode, the modifier destroys all information attempting to pass along the connection to which it is attached. Once set, a connection-modifier will maintain its function unless it receives a pulse on the other training fibre. The presence of these modifiers enables a B-type unorganised machine to be trained, by means of what Turing called ‘appropriate interference, mimicking education’.

Let’s try to construct such a connection in graphic lambda calculus. I shall use the notations from the previous post Teaser: B-type neural networks in graphic lambda calculus (I).

* 3. Connections. * In lambda calculus, Church booleans are the following terms: and (remark that is the combinator ). By using the algorithm for transforming lambda calculus terms into graphs in , we obtain the following graphs:

They act on other graphs () like this:

The graphs are almost identical: they are both made by a 2-zipper with an additional termination gate and a wire. See the post Combinators and zippers for more explanations about , or .

I am going to exploit this structure in the construction of a connection. We are going to need the following ingredients: a 2-zipper, an INPUT BOX (otherwise called “switch”, see further) and an OUTPUT BOX,

which is almost identical with a switch (it is identical as a graph, but we are going to connect it with other graphs at each labelled edge):

I start with the following description of objects and moves from the freedom sector of graphic lambda calculus (the magenta triangles were used also in the previous post). I call the object from the middle of the picture a switch.

As you can see, a switch can be transformed into one of the two graphs (up and down parts of the figure). We can exploit the switch in relation with the and graphs. Indeed, look at the next figure, which describes graphs almost identical with the and graph (as represented by using zippers), with an added switch:

Now we are ready for describing a connection like the one from the B-type neural networks (only that better, because it’s done in graphic lambda calculus, thus much more expressive than boolean expressions). Instead of training the connection by a boolean TRUE of FALSE input (coming by one of the green or red wires in the first figure of the post), we replace the connection by an OUTPUT BOX (should I call it “synapse”? I don’t know yet) which is controlled by a switch. The graph of a connection is the following:

The connection between an axon and a dendrite is realized by having the axon at “1” and the dendrite at “3”. We may add a termination gate at “2”, but this is irrelevant somehow. At the top of the figure we have a switch, which can take any of the two positions corresponding, literary, to or . This will transform the OUTPUT BOX into one of the two possible graphs which can be obtained from a switch.

You may ask why did I not put directly a switch instead of an OUTPUT BOX. Because, in this way, the switch itself may be replaced by the OUTPUT BOX of another connection. The second reason is that by separating the graph of the connection into a switch, a 2-zipper and an OUTPUT BOX, I proved that what is making the switch to function is the TRUE-FALSE like input, in a rigorous way. Finally, I recall that in graphic lambda calculus the green dashed ovals are only visual aids, without intrinsic significance. By separating the OUTPUT BOX from the INPUT BOX (i.e. the switch) with a zipper, the graph has now an unambiguous structure.

## 3 thoughts on “Teaser: B-type neural networks in graphic lambda calculus (II)”