A classic "Hopfield network" is a type of artificial neural network in which the units are bi-stable and fully interconnected by symmetrically weighted connections. In 1982, Hopfield showed that such networks are characterized by an "energy function", under which stored memories correspond to local energy minima .
In a 1983 paper , Hopfield et al further showed that "spurious memories" (local energy minima that are created during training, in addition to the intended target patterns) can be suppressed by an "unlearning procedure", during which the network is repeatedly allowed to relax from random states, and the resulting states then "unlearned" by anti-Hebbian weight adjustments. The procedure affects spurious memories more than the desirable "learned memories", thus improving recall performance. However, the paper offers no explanation for why this should be so.
A 2004 paper by Robins and McCallum  demonstrates that spurious memories can be distinguished from learned ones because their "energy profiles" are different. Specifically, the ratio of lowest to highest energy contributions from individual units is significantly smaller in states corresponding to spurious memories than in states corresponding to learned memories. Again, the effect is not accounted for (except for a tentative partial explanation).
My questions are:
- Is there a relationship between these two findings, i.e. does the lower "energy ratio" of spurious states explain their greater susceptibility to unlearning?
- Have any explanations for either or both of these phenomena been put forward since the publication of the papers?
- Are there other ways to suppress or detect spurious memories in the Hopfield family of neural networks?
 Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554 –2558.
 Hopfield, J. J., Feinstein, D. I., & Palmer, R. G. (1983). “Unlearning” has a stabilizing effect in collective memories. Nature, 304(5922), 158–159.
 Robins, A. V., & McCallum, S. J. R. (2004). A robust method for distinguishing between learned and spurious attractors. Neural Networks, 17(3), 313–326. doi:10.1016/j.neunet.2003.11.007