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Universal Learning Neurocomputers

A universal learning neurocomputer is a brain– inspired information processing system that can be taught to perform, in principle, an arbitrary algorithm (universal in Turing’s sense). Conventional computers and universal Turing machines are examples of universal programmable systems. However, the process of programming these systems does not match our intuitive notion of teaching and learning as applicable to the brain. Some of the presently popular neural network models seem to qualify as brainlike learning systems. However, these models are not universal in Turing’s sense, and can be taught to perform only very limited classes of functions. For example, the computing power of the perceptron–like feedforward networks does not exceed that of combinatorial machines. Accordingly, the computing power of such networks with a feedback can not be greater than the computing power of finite–state machines. In contrast, the human brain is ”universal” and ”learning” at the same time. What is the simplest architecture of a universal learning neurocomputer? This paper is an attempt to address this basic issue.