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Mind, Body, World- Foundations of Cognitive Science, 2013a

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= 0.5<br />

<br />

-2<br />

1<br />

1<br />

1<br />

= 1.5<br />

<br />

1<br />

p<br />

q<br />

Figure 4-4. A multilayer perceptron that can compute XOR.<br />

The action <strong>of</strong> the hidden unit is crucial to the behaviour <strong>of</strong> the system. When neither<br />

or only one <strong>of</strong> the input units activates, the hidden unit does not respond, so it sends<br />

a signal <strong>of</strong> 0 to the output unit. As a result, in these three situations the output unit<br />

turns on when either <strong>of</strong> the inputs is on (because the net input is over the threshold)<br />

and turns <strong>of</strong>f when neither input unit is on. When both input units are on, they send<br />

an excitatory signal to the output unit. However, they also send a signal that turns<br />

on the hidden unit, causing it to send inhibition to the output unit. In this situation,<br />

the net input <strong>of</strong> the output unit is 1 + 1 – 2 = 0 which is below threshold, producing<br />

zero output unit activity. The entire circuit therefore performs the XOR operation.<br />

The behaviour <strong>of</strong> the Figure 4-4 multilayer perceptron can also be related to the<br />

pattern space <strong>of</strong> Figure 4-2B. The lower cut in that pattern space is provided by the<br />

output unit. The upper cut in that pattern space is provided by the hidden unit. The<br />

coordination <strong>of</strong> the two units permits the circuit to solve this linearly nonseparable<br />

problem.<br />

Interpreting networks in terms <strong>of</strong> the manner in which they carve a pattern<br />

space into decision regions suggests that learning can be described as determining<br />

where cuts in a pattern space should be made. Any hidden or output unit that uses<br />

a nonlinear, monotonic function like the Heaviside or the logistic can be viewed<br />

as making a single cut in a space. The position and orientation <strong>of</strong> this cut is determined<br />

by the weights <strong>of</strong> the connections feeding into the unit, as well as the threshold<br />

or bias () <strong>of</strong> the unit. A learning rule modifies all <strong>of</strong> these components. (The<br />

bias <strong>of</strong> a unit can be trained as if it were just another connection weight by assuming<br />

that it is the signal coming from a special, extra input unit that is always turned on<br />

[Dawson, 2004, 2005].)<br />

146 Chapter 4

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