  Transitions between metastable states are commonly observed in the neural
system and underlie various cognitive functions such as working memory. In a
previous study, we have developed a neural network model with the slow and fast
populations, wherein simple Hebb-type learning enables stable and complex
(e.g., non-Markov) transitions between neural states. This model is distinct
from a network with asymmetric Hebbian connectivity and a network trained with
supervised machine learning methods: the former generates simple Markov
sequences. The latter generates complex but vulnerable sequences against
perturbation and its learning methods are biologically implausible. By using
our model, we propose and demonstrate a novel mechanism underlying stable
working memories: sequentially stabilizing and destabilizing task-related
states in the fast neural dynamics. The slow dynamics maintain a history of the
applied inputs, e.g., context signals, and enable the task-related states to be
stabilized in a context-dependent manner. We found that only a single (or a
few) state(s) is stabilized in each epoch (i.e., a period in the presence of
the context signal and a delayed period) in a working memory task, resulting in
a robust performance against noise and change in a task protocol. These results
suggest a simple mechanism underlying complex and stable processing in neural
systems.
