  We introduce a recurrent neural network model of working memory combining
short-term and long-term components. e short-term component is modelled using a
gated reservoir model that is trained to hold a value from an input stream when
a gate signal is on. e long-term component is modelled using conceptors in
order to store inner temporal patterns (that corresponds to values). We combine
these two components to obtain a model where information can go from long-term
memory to short-term memory and vice-versa and we show how standard operations
on conceptors allow to combine long-term memories and describe their effect on
short-term memory.
