Creating Network Motifs with Developmental Graph Cellular Automata

Analysing network motifs is a common way of characterising biological networks. Motifs are small subgraphs that are more abundant in the observed network than would be expected in random graphs. They may play an important role in network function, and as such may be selected by evolution. In some cases, such as neural networks, they are instantiated via a developmental process. The processes used to structure Artificial Neural Networks, whether training or evolution, do not usually result in motifs or modularity more generally. We introduce a new version of Developmental Graph Cellular Automata (DGCA) which can be used in an evolutionary and developmental (evo-devo) process to produce networks with specific motif profiles. We evolve developmental rules (the “genome”) so that networks are produced with similar motif profiles to specific biological networks. Networks produced in this way may have useful computational and/or dynamical properties when deployed as Recurrent Neural Networks (RNNs) or in Reservoir Computing (RC).