TY - GEN
T1 - A Computational Model for a Multi-Goal Spatial Navigation Task inspired by Rodent Studies
AU - Llofriu, Martin
AU - Scleidorovich, Pablo
AU - Tejera, Gonzalo
AU - Contreras, Marco
AU - Pelc, Tatiana
AU - Fellous, Jean Marc
AU - Weitzenfeld, Alfredo
N1 - Funding Information:
NSF IIS Robust Intelligence research collaboration grant #1117303
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - We present a biologically-inspired computational model of the rodent hippocampus based on recent studies of the hippocampus showing that its longitudinal axis is involved in complex spatial navigation. While both poles of the hippocampus, i.e. septal (dorsal) and temporal (ventral), encode spatial information; the septal area has traditionally been attributed more to navigation and action selection; whereas the temporal pole has been more involved with learning and motivation. In this work we hypothesize that the septal-temporal organization of the hippocampus axis also provides a multi-scale spatial representation that may be exploited during complex rodent navigation. To test this hypothesis, we developed a multi-scale model of the hippocampus evaluated it with a simulated rat on a multi-goal task, initially in a simplified environment, and then on a more complex environment where multiple obstacles are introduced. In addition to the hippocampus providing a spatial representation of the environment, the model includes an actor-critic framework for the motivated learning of the different tasks.
AB - We present a biologically-inspired computational model of the rodent hippocampus based on recent studies of the hippocampus showing that its longitudinal axis is involved in complex spatial navigation. While both poles of the hippocampus, i.e. septal (dorsal) and temporal (ventral), encode spatial information; the septal area has traditionally been attributed more to navigation and action selection; whereas the temporal pole has been more involved with learning and motivation. In this work we hypothesize that the septal-temporal organization of the hippocampus axis also provides a multi-scale spatial representation that may be exploited during complex rodent navigation. To test this hypothesis, we developed a multi-scale model of the hippocampus evaluated it with a simulated rat on a multi-goal task, initially in a simplified environment, and then on a more complex environment where multiple obstacles are introduced. In addition to the hippocampus providing a spatial representation of the environment, the model includes an actor-critic framework for the motivated learning of the different tasks.
KW - computational neuroscience
KW - learning
KW - navigation
KW - neural networks
KW - spatial cognition
UR - http://www.scopus.com/inward/record.url?scp=85073260699&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073260699&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2019.8851852
DO - 10.1109/IJCNN.2019.8851852
M3 - Conference contribution
AN - SCOPUS:85073260699
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2019 International Joint Conference on Neural Networks, IJCNN 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Joint Conference on Neural Networks, IJCNN 2019
Y2 - 14 July 2019 through 19 July 2019
ER -