With network ANP decision structures we raise the weighted supermatrix to powers to obtain the limiting priorities that represent the answer we seek, the priorities of the alternatives of choice. With a neural structure we need eigenfuntions to represent the priorities. The firing of each neuron can be represented by an eigenfunction, an exponentially damped periodic function of period one. There are four fundamental kinds of eigenfunctions: real, complex, quaternionic and octonionic. Each kind is suitable for certain kinds of sensations and thoughts. Interpreting how they contribute to consciousness is under investigation in a paper to appear in the journal Neural Networks.
AHP, ANP, decision making, neural networks, eigenfunctions
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