A reconfigurable network architecture applied to spiking neural networks is presented. For hardware platforms for neural networks that implement some degree of realism of interest to neuroscientists, connectivity between neurons can be a major limitation. Recent data indicates that neurons in the brain form clusters of connections. Through the combination of this data and a routing scheme that uses a hybrid of short-range direct connectivity and an AER (Address Event Representation) network, the presented architecture aims to provide a useful amount of inter-neuron connectivity. A connection-centric design can provide opportunities for NoCs such as optimising power, bandwidth or introducing redundancy. A method of mapping a network to the architecture is discussed, along with results of optimal hardware specifications for a given set of network parameters.