Multiscale modeling of developmental processes

Abstract

In contrast to most synthetic neural nets, biological neural networks have a strong component of determination which acts before and during experiential learning. Three broad levels of phenomena are present: long-term evolution, involving crossover as well as point mutation; a developmental process mapping information to a set of cells and their internal states of gene expression (genotype to phenotype); and the subsequent synaptogenesis. We describe a very simple mathematical idealization of these three levels which combines the crossover search method of algorithms with the developmental models used in our previous work on genetic or recursively generated artificial neural nets [18] (and elaborated into a connectionist model of biological development [19]). Despite incorporating all three levels (evolution on genes; development of cells; synapse formation) the model may actually be far cheaper to compute with than a comparable search directly in synaptic weight space.