Emergence of Structure in a Recurrent Network with Anisotropic Spatial Connectivity

Abstract: Today homogeneous Locally Connected Random Networks are often used while simulating activity in the brain. The two possible activation patterns in this case are either a stand still activation or a travelling wave. The activation pattern in the brain could however be seen as something else. By introducing inhomogeneity and anisotropy in the connectivity for neurons one can create small feed forward networks in an otherwise random network. By using Perlin noise one can create connectivity rules where there is direction of connectivity and neighbouring neurons have similar rules. In this study it was investigated if these modified LCRNs have the structural properties communities and feed forward chains. Three types of networks, differentiated by distinct connectivity rules, were analysed. These were networks with isotropic connections, connection with a preferred direction with all directions chosen independently and connection with a preferred direction with directions chosen in a way that neighbouring nodes have similar direction. The study shows that these structural properties exists, as well as a correlation between them.