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Abstract

The refinement of machine learning strategies and deep convolutional networks led to the development of artificial systems whose functions resemble those of natural brains, suggesting that the two systems share the same computational principles. In this chapter, evidence is reviewed which indicates that the computational operations of natural systems differ in some important aspects from those implemented in artificial systems. Natural processing architectures are characterized by recurrence and therefore exhibit high-dimensional, non-linear dynamics. Moreover, they use learning mechanisms that support self-organization. It is proposed that these properties allow for computations that are notoriously difficult to realize in artificial systems. Experimental evidence on the organization and function of the cerebral cortex is reviewed that supports this proposal.