Name
Title | ||
|---|---|---|
Deciphering the contributions of episodic and working memories in increasingly complex decision tasks |
Abstract | ||
|---|---|---|
Augmenting the representation of the current state of the external world with internal states corresponding to working and episodic memories has been proposed as a bio-inspired solution to apply models of reinforcement learning to non-Markovian tasks. But, transposing these results to behavioral and experimental neuroscience, it is not completely clear how each of these memories can contribute to learning the augmented representations and when they must act in association for more complex tasks. Choosing an elementary implementation of these memories and experimental tasks of decision making in rodents, we explore these pivotal situations and make concrete the underlying mechanisms and criteria. We also specify cases where additional mechanisms must be envisaged. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9534315 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Keywords | DocType | ISSN |
reinforcement learning, working memory, episodic memory | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Snigdha Dagar | 1 | 0 | 0.34 |
| Frédéric Alexandre | 2 | 82 | 15.94 |
| Nicolas P. Rougier | 3 | 106 | 14.78 |