Abstract
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).
| Original language | English |
|---|---|
| Title of host publication | Artificial Life Conference Proceedings |
| Publisher | MIT Press |
| Number of pages | 9 |
| DOIs | |
| Publication status | Published - 22 Jul 2024 |
| Event | ALife 2024: The 2024 Conference on Artificial Life - IT University of Copenhagen, Copenhagen, Denmark Duration: 22 Jul 2024 → 26 Jul 2024 https://2024.alife.org/ |
Publication series
| Name | ALIFE : proceedings of the artificial life conference |
|---|---|
| Publisher | MIT |
| ISSN (Print) | 2693-1508 |
Conference
| Conference | ALife 2024 |
|---|---|
| Country/Territory | Denmark |
| City | Copenhagen |
| Period | 22/07/24 → 26/07/24 |
| Internet address |
Keywords
- graph cellular automata
- network motifs
- neural networks
- artificial evolution
- artificial development
- evo-devo