Discrete Hopfield neural network (DHNN) is studied by performing permutatio
n operations on the synaptic weight matrix. The storable patterns set store
d with Hebbian learning algorithm in a network without losing memories is s
tudied, and a condition which makes sure all the patterns of the storable p
atterns set have a same basin size of attraction is proposed. Then, the per
mutation symmetries of the network are studied associating with the stored
patterns set. A construction of the storable patterns set satisfying that c
ondition is achieved by consideration of their invariance under a point gro
up.