  Neural network models have been used to construct energy landscapes for
modeling biological phenomena, in which the minima of the landscape correspond
to memory patterns stored by the network. Here, we show that dynamic properties
of those landscapes, such as the sizes of the basins of attraction and the
density of stable and metastable states, depend strongly on the correlations
between the memory patterns and can be altered by introducing hierarchical
structures. Our findings suggest dynamic features of energy landscapes can be
controlled by choosing the correlations between patterns
