The entorhino-hippocampal neural circuits of the mammalian brains are able to internally generate efficient spatial representations of large-scale environments. Hippocampal principal cells in mammalian brains form sparse codes of the positions in large environments. The underlying computational mechanisms of the formation of spatial memory in large-scale environments are still not well understood. We present a competitive learning network that integrates both spatial inputs from grid cells as well as contextual inputs from non-spatial cells. Through self-organisation, place units in the network form place fields at “randomly” interleaved locations, in the sense that both the recruitment process and the inter-field interval are memoryless, a property superior for spatial memory formation in large environments.