| Paper: | GS-6.2 | ||
| Session: | Contributed Talks 11-12 | ||
| Location: | H0104 | ||
| Session Time: | Monday, September 16, 11:50 - 12:30 | ||
| Presentation Time: | Monday, September 16, 12:10 - 12:30 | ||
| Presentation: | Oral | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | A Memory-Augmented Reinforcement Learning Model of Food Caching Behaviour in Birds | ||
| Manuscript: | Click here to view manuscript | ||
| View Video: | Video | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1316-0 | ||
| Authors: | Johanni Brea, Wulfram Gerstner, EPFL, Switzerland | ||
| Abstract: | Birds of the crow family are well known for their complex cognition. In laboratory experiments it has been observed that jays adapt food caching strategies to anticipated needs and rely on a memory of the what, where and when of previous caching events for cache recovery. While this behaviour is well studied, little is known about the algorithms and neural processes that produce this behaviour. We present a computational model and propose a neural implementation of food caching behaviour. Our model features latent hunger variables for motivational control, an associative memory for snapshots of the sensory states during caching events, a system memory consolidation for flexible decoding of the age of a memory, a stimulus-driven retrieval mechanism, and reward-modulated update of retrieval and caching policies during inspection of caches. We show that our model is in quantitative agreement with the results of 22 behavioural experiments. Our methodology of a formalization of experimental protocols via a domain-specific language is transferable to other domains and may serve as a tool to design new experiments and foster collaboration between experimentalists and theoreticians. Our model is an example of a structured reinforcement learning algorithm that could have evolved in species that operate in partially observable environments. | ||