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Figure 9.7 depicts a single, sigmoidal unit used to simulate a unisensory neuron in the superior colliculus. The unit y receives input x and bias b. The weight matrix V in this case is a row vector containing the weights of the two connections to y, one from x and the other from b. The unit computes the weighted sum of its input and passes the result through the sigmoidal squashing function (see Chapter 6). The input x could represent the visual input to the collicular neuron, and bias b could represent a constant influence on neural firing rate due to the biophysical properties of the neuron or to nonspecific inputs that are constant in combination.
The equation below diagrams the anatomy of Bayes' rule.
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