Multiscale modeling of developmental processes. (English) Zbl 0895.92005
Summary: In contrast to most synthetic neural nets, biological neural networks have a strong component of genetic 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 genetic 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 genetic algorithms with the developmental models used in our previous work on “genetic” or “recursively generated” artificial neural nets [Adv. Appl. Math. 10, No. 2, 137-163 (1989; Zbl 0681.68104)] (and elaborated into a connectionist model of biological development). 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.
We describe a very simple mathematical idealization of these three levels which combines the crossover search method of genetic algorithms with the developmental models used in our previous work on “genetic” or “recursively generated” artificial neural nets [Adv. Appl. Math. 10, No. 2, 137-163 (1989; Zbl 0681.68104)] (and elaborated into a connectionist model of biological development). 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.
MSC:
| 92C15 | Developmental biology, pattern formation |
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
Keywords:
evolution; developmental process; synaptogenesis; crossover search method; genetic algorithmsCitations:
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