'GENOTYPES' FOR NEURAL NETWORKS Genetic algorithms applied to populations of neural networks can be viewed as an alternative methods for obtaining desired performances from networks with respect to more traditional learning methods. Or they can be interpreted as models of processes of neural evolution occurring in nature. If one takes this second stance, aspects of evolution which are ignored in most applications of genetic algorithms can become important objects of study. One such aspect relates to the distinction between genotype and phenotype. In almost all applications of genetic algorithms to neural networks what is genetically inherited is the phenotypic network itself whereas in nature genetic information specifies a set of instructions for constructing the phenotypic network, with the mapping from genotype to phenotype very indirect and nonlinear. This chapter describes a scheme for encoding 'genotypes' for neural networks which map in complex ways to the corresponding phenotypic network. An important aspect of the genotype/phenotype mapping is its temporal character. Biological development (or maturation) is the sequence of different phenotypic forms which are sequentially generated by the genotype in interaction with the environment. While most simulations with complex genotype/phenotype mappings ignore this aspect and generate a single mature network at birth, the chapter describes simulations with networks which change (grow) during the life of the organism. The distinction between genotype and phenotype and the realization of the mapping as a temporally distributed process make it possible to examine important questions of evolution and development and of their interaction.