Decomposing neural networks into systems of cooperating subnets

Decomposing a large network into a system of smaller subnets is explored. Each subnet is an independent network that performs some task. These subnets will be linked together in such a way as to collectively solve some larger task. Only the signals from the output units of a subnet are allowed to connect to the inputs of other subnets. That is, no connections will be made from within one subnet to another. Training is applied to each subnet independently. Advantages include distributed processing, reduced number of interconnections, and resource sharing. The consequences of pursuing such a system are investigated