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

ecording or wiretapping (Calvin & Ojemann, 1994), in which the responses of single
neurons are measured while stimuli are being presented to an animal. With this
technique, it is possible to describe a neuron as being sensitive to a trigger feature, a
specific pattern that when detected produces maximum activity in the cell.
That individual neurons may be described as detecting trigger features has led
some to endorse a neuron doctrine for perceptual psychology. This doctrine has the
goal of discovering the trigger features for all neurons (Barlow, 1972, 1995). This is
because,
a description of that activity of a single nerve cell which is transmitted to and influences
other nerve cells, and of a nerve cell’s response to such influences from other
cells, is a complete enough description for functional understanding of the nervous
system. (Barlow, 1972, p. 380)
The validity of the neuron doctrine is a controversial issue (Bowers, 2009;
Gross, 2002). Regardless, there is a possibility that identifying trigger features can
help to interpret the internal workings of artificial neural networks.
For some types of hidden units, trigger features can be identified analytically,
without requiring any wiretapping of hidden unit activities (Dawson, 2004).
For instance, the activation function for an integration device (e.g., the logistic
equation) is monotonic, which means that increases in net input always produce
increases in activity. As a result, if one knows the maximum and minimum possible
values for input signals, then one can define an integration device’s trigger
feature simply by inspecting the connection weights that feed into it (Dawson,
Kremer, & Gannon, 1994). The trigger feature is that pattern which sends the minimum
signal through every inhibitory connection and the maximum signal through
every excitatory connection. The monotonicity of an integration device’s activation
function ensures that it will have only one trigger feature.
The notion of a trigger feature for other kinds of hidden units is more complex.
Consider a value unit whose bias, m, in its Gaussian activation function is equal
to 0. The trigger feature for this unit will be the feature that causes it to produce
maximum activation. For this value unit, this will occur when the net input to the
unit is equal to 0 (i.e., equal to the value of ) (Dawson & Schopflocher, 1992b). The
net input of a value unit is defined by a particular linear algebra operation, called
the inner product, between a vector that represents a stimulus and a vector that
represents the connection weights that fan into the unit (Dawson, 2004). So, when
net input equals 0, this means that the inner product is equal to 0.
However, when an inner product is equal to 0, this indicates that the two vectors
being combined are orthogonal to one another (that is, there is an angle of 90°
between the two vectors). Geometrically speaking, then, the trigger feature for a
value unit is an input pattern represented by a vector of activities that is at a right
angle to the vector of connection weights.
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