Modularity as a Solution to Spatial Interference in Neural Networks

Abstract

Modularity is an architectural trait that is prominent in biological neural networks, but strangely absent in evolved artificial neural networks. This report contains the results of a theoretical study focusing on two questions about modularity in neural network systems. How does modularity emerge in biological neural networks, and when could modularity be useful in artificial neural networks?The theoretical study resulted in a hypothesis that modularity in biological neural networks is the result of physical constraints on their architectures. Because these physical constraints affect the digital environments in a different way, modularity does not emerge naturally during evolution of neural networks in a digital medium. Secondly, it is hypothesised that modularity in artificial neural networks can reduce the amount of spatial interference during learning. A phenomenon that is here shown to occur when two outputs that exhibit low correlation are solved using the same neural network structures.Experiments have been performed in order to verify if there are advantages to having modular topologies in order to limit the detrimental effects of spatial interference occurring during learning in artificial neural networks.