It is shown that approximate fixed-point attractors rather than synchr
onized oscillations can be employed by a wide class of neural networks
of oscillators to achieve an associative memory recall. This computat
ional ability of oscillator neural networks is ensured by the fact tha
t reduced dynamic equations for phase variables in general involve two
terms that can be respectively responsible for the emergence of synch
ronization and cessation of oscillations. Thus the cessation occurs in
memory retrieval if the corresponding term dominates in the dynamic e
quations. A bottomless feature of the energy function for such a syste
m makes the retrieval states quasi-fixed points, which admit continual
rotating motion to a small portion of oscillators, when an extensive
number of memory patterns are embedded. An approximate theory based on
the self-consistent signal-to-noise analysis enables one to study the
equilibrium properties of the neural network of phase variables with
the quasi-fixed-point attractors. As far as the memory retrieval by th
e quasi-fixed points is concerned, the equilibrium properties includin
g the storage capacity of oscillator neural networks are proved to be
similar to those of the Hopfield type neural networks.