  Anatomical studies demonstrate that brain reformats input information to
generate reliable responses for performing computations. However, it remains
unclear how neural circuits encode complex spatio-temporal patterns. We show
that neural dynamics are strongly influenced by the phase alignment between the
input and the spontaneous chaotic activity. Input structuring along the
dominant chaotic projections causes the chaotic trajectories to become stable
channels (or attractors), hence, improving the computational capability of a
recurrent network. Using mean field analysis, we derive the impact of input
structuring on the overall stability of attractors formed. Our results indicate
that input alignment determines the extent of intrinsic noise suppression and
hence, alters the attractor state stability, thereby controlling the network's
inference ability.
