TY - GEN
T1 - A Computational Model for Latent Learning based on Hippocampal Replay
AU - Scleidorovich, Pablo
AU - Llofriu, Martin
AU - Fellous, Jean Marc
AU - Weitzenfeld, Alfredo
N1 - Funding Information:
ACKNOWLEDGMENT This work is funded by NSF IIS Robust Intelligence research collaboration grant #1703340 at USF and U. Arizona entitled “RI: Medium: Collaborative Research: Experimental and Robotics Investigations of Multi-Scale Spatial Memory Consolidation in Complex Environments”.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - We show how hippocampal replay could explain latent learning, a phenomenon observed in animals where unrewarded pre-exposure to an environment, i.e. habituation, improves task learning rates once rewarded trials begin. We first describe a computational model for spatial navigation inspired by rat studies. The model exploits offline replay of trajectories previously learned by applying reinforcement learning. Then, to assess our hypothesis, the model is evaluated in a multiple T-maze environment where rats need to learn a path from the start of the maze to the goal. Simulation results support our hypothesis that pre-exposed or habituated rats learn the task significantly faster than non-pre-exposed rats. Results also show that this effect increases with the number of pre-exposed trials.
AB - We show how hippocampal replay could explain latent learning, a phenomenon observed in animals where unrewarded pre-exposure to an environment, i.e. habituation, improves task learning rates once rewarded trials begin. We first describe a computational model for spatial navigation inspired by rat studies. The model exploits offline replay of trajectories previously learned by applying reinforcement learning. Then, to assess our hypothesis, the model is evaluated in a multiple T-maze environment where rats need to learn a path from the start of the maze to the goal. Simulation results support our hypothesis that pre-exposed or habituated rats learn the task significantly faster than non-pre-exposed rats. Results also show that this effect increases with the number of pre-exposed trials.
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U2 - 10.1109/IJCNN48605.2020.9206824
DO - 10.1109/IJCNN48605.2020.9206824
M3 - Conference contribution
AN - SCOPUS:85093860688
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Joint Conference on Neural Networks, IJCNN 2020
Y2 - 19 July 2020 through 24 July 2020
ER -