Spatial locations are thought to be encoded in the rat hippocampus as positions on 2D continuous attractors, each representing (a chart of) a distinct evironment. In a simple recurrent neural network with several discrete attractor states, population activity tends to align with the closest attractor, so that slightly crossing the boundary between two basins of attraction can lead to sudden transitions in the final state. Leutgeb et al (SfN 2004) aimed to detect such attractor states in CA3 activity by first training rats to forage in a square and in a circle box, and the testing them in 5 intermediate morph shapes. Unexpectedly, activity in the morphs changed gradually, interpolating between the representations of the extremes. We ran computer simulations on a reduced hippocampal model (Treves, Hippocampus 2004) to test the hypothesis that the initially well separated attractors at the extremes might have been stretched by ongoing plasticity to accomodate intermediate shapes. In the simulations, we could ...