Structured neural network topologies with application to acoustic transients

The use of structured interconnection topologies is considered for designing layered feedforward neural networks when no explicit information on the application data space is available at design time. Sparse, structured designs can yield computationally efficient networks when the input and output data spaces are large. In particular, the fully connected, cube connected, and butterfly connected networks are analyzed, and their interconnection characteristics are compared. The tradeoff incurred in making the incidence matrix increasingly more sparse is noted, and it is concluded that an acceptable design should be moderately interconnected, allowing a reduction in synaptic density while maintaining functional flexibility