1. A method of training a computational network, comprising: computing an output of a processing unit in the computational network based at least in part on at least one existing weight; and changing the at least one weight of the processing unit using a local training rule, wherein the local training rule creates sparse connectivity between processing units of the computational network by constraining a norm of a weight vector comprising weights associated with connections between the processing units to a defined value when an average change of the weights equals zero, wherein the computational network comprises a neural network, and the processing unit comprises a neuron circuit.