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

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in the layer. Patterns are again represented as points in this space; however, in this<br />

space their coordinates are determined by the activities they produce in each hidden<br />

unit. The hidden unit space is a transformation <strong>of</strong> the pattern space that involves<br />

detecting higher-order features. This usually produces a change in dimensionality—the<br />

hidden unit space <strong>of</strong>ten has a different number <strong>of</strong> dimensions than does<br />

the pattern space—and a repositioning <strong>of</strong> the points in the new space. As a result,<br />

the output units are able to carve the hidden unit space into a set <strong>of</strong> decision regions<br />

that permit all <strong>of</strong> the patterns, repositioned in the hidden unit space, to be correctly<br />

classified.<br />

This account <strong>of</strong> the role <strong>of</strong> hidden units indicates that the interpretation <strong>of</strong> the<br />

internal structure <strong>of</strong> a multilayer perceptron involves answering two different questions.<br />

First, what kinds <strong>of</strong> features are the hidden units detecting in order to map patterns<br />

from the pattern space into the hidden unit space? Second, how do the output<br />

units process the hidden unit space to solve the problem <strong>of</strong> interest? The chord classification<br />

network can be used to illustrate how both questions can be addressed.<br />

First, when mapping the input patterns into the hidden unit space, the hidden<br />

units must be detecting some sorts <strong>of</strong> musical regularities. One clue as to what these<br />

regularities may be is provided by simply examining the connection weights that<br />

feed into them, provided in Table 4-2.<br />

Input<br />

Note<br />

Hidden 1 Hidden 1<br />

Class<br />

Hidden 2 Hidden 2<br />

Class<br />

B<br />

D#<br />

0.53<br />

0.53<br />

Circle <strong>of</strong> Major<br />

0.12<br />

0.12<br />

Circle <strong>of</strong> Major<br />

0.75<br />

0.75<br />

G 0.53<br />

Thirds 1<br />

0.12<br />

Thirds 1<br />

0.75<br />

A –0.53<br />

Circle <strong>of</strong> Major<br />

–0.12<br />

Circle <strong>of</strong> Major<br />

0.75<br />

C# –0.53 –0.12 0.75<br />

F –0.53<br />

Thirds 2<br />

–0.12<br />

Thirds 2<br />

0.75<br />

C 0.12<br />

Circle <strong>of</strong> Major<br />

–0.53<br />

Circle <strong>of</strong> Major<br />

–0.77<br />

G# 0.12 –0.53 –0.77<br />

E 0.12<br />

Thirds 3<br />

–0.53<br />

Thirds 3<br />

–0.77<br />

F# –0.12<br />

Circle <strong>of</strong> Major<br />

0.53<br />

Circle <strong>of</strong> Major<br />

–0.77<br />

A# –0.12 0.53 –0.77<br />

D –0.12<br />

Thirds 4<br />

0.53<br />

Thirds 4<br />

–0.77<br />

Hidden 3 Hidden 3<br />

Class<br />

Circle <strong>of</strong><br />

Major<br />

Seconds 1<br />

Circle <strong>of</strong><br />

Major<br />

Seconds 2<br />

Table 4-2. Connection weights from the 12 input units to each <strong>of</strong> the three<br />

hidden units. Note that the first two hidden units adopt weights that assign<br />

input notes to the four circles <strong>of</strong> major thirds. The third hidden unit adopts<br />

weights that assign input notes to the two circles <strong>of</strong> major seconds.<br />

166 Chapter 4

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