The recurrent synaptic weights within the network of Head Direction (HD) cells after training with the Hebbian associative learning rule (11) and weight normalization (13).

<p>These results are from a simulation with an axonal conduction delay of 100 ms, and a rotational velocity during training of (all other parameters are as given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058330#pone-0058330-t001" target="_blank">Table 1</a>). Each of the four plots shows the learned synaptic weights to a different postsynaptic HD cell from the other 500 presynaptic HD cells in the network. In the plots, the 500 presynaptic HD cells are arranged according to where they fire maximally in the head-direction space of the agent when visual input is available. For each plot, a dashed vertical line indicates the presynaptic HD cell with which the postsynaptic HD cell has maximal synaptic weight. In all plots, the synaptic weight profile is symmetric about the presynaptic HD cell with maximal synaptic strength, and this symmetry helps to support a stable packet of HD cell activity during testing in the absence of visual input.