INTERPRETABLE NEURAL NETWORK

A method of operating a neural network, comprising: at each input node of an input layer, weighting a respective input element received by that node by applying a first class of probability distribution, thereby generating a respective set of output parameters describing an output probability distribution; and from each input node, outputting the respective set of output parameters to one or more nodes in a next, hidden layer of the network, thereby propagating the respective set of output parameters through the hidden layers to an output layer; the propagating comprising, at one or more nodes of at least one hidden layer, combining the sets of input parameters and weighting the combination by applying a second class of probability distribution, thereby generating a respective set of output parameters describing an output probability distribution, wherein the first class of probability distribution is more sparsity inducing than the second class of probability distribution.