A recurrent neural network framework for flexible and adaptive decision making based on sequence learning.

ABSTRACT: The brain makes flexible and adaptive responses in a complicated and ever-changing environment for an organism's survival. To achieve this, the brain needs to understand the contingencies between its sensory inputs, actions, and rewards. This is analogous to the statistical inference that has been extensively studied in the natural language processing field, where recent developments of recurrent neural networks have found many successes. We wonder whether these neural networks, the gated recurrent unit (GRU) networks in particular, reflect how the brain solves the contingency problem. Therefore, we build a GRU network framework inspired by the statistical learning approach of NLP and test it with four exemplar behavior tasks previously used in empirical studies. The network models are tra

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC7673505 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature