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.
@article{arxiv.1712.10062,
title = {Multi-timescale memory dynamics in a reinforcement learning network with attention-gated memory},
author = {Marco Martinolli and Wulfram Gerstner and Aditya Gilra},
journal= {arXiv preprint arXiv:1712.10062},
year = {2019}
}