Nature, nurture, and the development of functional specializations: A computational approach

This article studies the hypothesis that a combination of structure-function correspondences plus the use of competition between modules leads to functional specializations in a family of neural network architectures referred to as “mixtures-ofexperts” architectures.

Abstract

The roles assigned to nature and nurture in the acquisition of functional specializations have been modified in recent years due to increasing evidence that experience-dependent processes are more influential in determining a brain region’s functional properties than was previously supposed. Consequently, one may study the developmental principles that play a role in the acquisition of functional specializations. This article studies the hypothesis that a combination of structure-function correspondences plus the use of competition between modules leads to functional specializations. This principle has been instantiated in a family of neural network architectures referred to as “mixtures-ofexperts” architectures. These architectures are sensitive to structure-function relationships in the sense that they often learn to allocate to each task a network whose structure is well matched to that task. The viewpoint advocated here represents a middle ground between nativist and constructivist views of modularity.