Can computational efficiency alone drive the evolution of modularity in neural networks?

ABSTRACT: Some biologists have abandoned the idea that computational efficiency in processing multipart tasks or input sets alone drives the evolution of modularity in biological networks. A recent study confirmed that small modular (neural) networks are relatively computationally-inefficient but large modular networks are slightly more efficient than non-modular ones. The present study determines whether these efficiency advantages with network size can drive the evolution of modularity in networks whose connective architecture can evolve. The answer is no, but the reason why is interesting. All simulations (run in a wide variety of parameter states) involving gradualistic connective evolution end in non-modular local attractors. Thus while a high performance modular attractor exists, such regions

SUBMITTER: Tosh CR 

PROVIDER: S-EPMC5004152 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature