Title: Multi-timescale memory dynamics in a reinforcement learning network with attention-gated memory,
Abstract: Learning and memory are intertwined in our brain and their relationship is at
the core of several recent neural network models. In particular, the
Attention-Gated MEmory Tagging model (AuGMEnT) is a reinforcement learning
network with an emphasis on biological plausibility of memory dynamics and
learning. We find that the AuGMEnT network does not solve some hierarchical
tasks, where higher-level stimuli have to be maintained over a long time, while
lower-level stimuli need to be remembered and forgotten over a shorter
timescale. To overcome this limitation, we introduce hybrid AuGMEnT, with leaky
or short-timescale and non-leaky or long-timescale units in memory, that allow
to exchange lower-level information while maintaining higher-level one, thus
solving both hierarchical and distractor tasks.