Artificial Neural Networks and Information Theory by Fyfe C.

By Fyfe C.

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We will consider the network as a transformation from inputs x to outputs z; by considering the effects of these rules on individual neurons, we can show that the resultant network is equivalent to Oja’s Subspace Algorithm. 15) k where we have dropped the time (t) notation for simplicity. 14). The comparative results given in the various tables in Chapter 3 were from a negative feedback network. 1 Biological Interneurons Because this network is similar to that found in biological networks, we have in the past called the network “The Interneuron Network”: there are in the cortex negative feedback neurons called interneurons which inhibit the neurons which cause them to fire.

The shortest distances, ri , will be minimised by the Total Least Squares method. g. that the surface is linear or quadratic, smooth or disjoint etc.. The accuracy of the results achieved will test the validity of our assumptions. This can be more formally stated as: let (X,Y) be a pair of random variables such that X ∈ Rn , Y ∈ R. , p. The usual method of forming the optimal surface is the Least (Sum of) Squares Method which minimises the Euclidean distance between the actual value of y and the estimate of y based on the current input vector, x.

The first Principal Component is most strongly identifying animal type features: animals tend to be big, have 4 legs and hair; some have hooves or a mane; somewhat more hunt and run. • The second Principal Component completes the job: the birds all are represented by a negative component as are the small, medium, 2 legs, feathers, hunt, fly and swim attributes. g. |fly| > |swim| since the prototypical bird is more likely to fly than swim. Note also that the cat has a small negative value brought about by its prototypical bird-like attribute of smallness.

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