  Despite the significance of short-term memory in cognitive function, the
process of encoding and sustaining the input information in neural activity
dynamics remains elusive. Herein, we unveiled the significance of transient
neural dynamics to short-term memory. By training recurrent neural networks to
short-term memory tasks and analyzing the dynamics, the characteristics of the
short-term memory mechanism were obtained in which the input information was
encoded in the amplitude of transient oscillations, rather than the stationary
neural activities. This transient trajectory was attracted to a slow manifold,
which permitted the discarding of irrelevant information. Additionally, we
investigated the process by which the dynamics acquire robustness to noise. In
this transient oscillation, the robustness to noise was obtained by a strong
contraction of the neural states after perturbation onto the manifold. This
mechanism works for several neural network models and tasks, which implies its
relevance to neural information processing in general.
