Self-assembled nanowire networks with memristive junctions represent arguably the closest hardware architecture to real biological neural networks and are thus uniquely placed to demonstrate genuinely neuromorphic information processing. We present preliminary results on polymer-coated silver nanowire networks. Their neuromorphic topology gives rise to a rich repertoire of collective nonlinear dynamics manifested through adaptive current transport pathways. The potential for associative learning is demonstrated in a test protocol in which a nanowire network is stimulated by multiple electrodes mapped to different spatial patterns. The capacity to process information in the temporal domain is demonstrated via simulations of a reservoir computing implementation in which nanowire networks are shown to perform several tasks, including waveform generation, time series prediction and handwritten digit recognition. Overall, their unique properties and neuromorphic information processing capabilities make nanowire networks promising candidates for emerging applications in cognitive devices in particular, at the edge.