Efficient neural decoding of self-location with a deep recurrent network.
Place cells in the mammalian hippocampus signal self-location with sparse spatially stable firing fields. Based on observation of place cell activity it is possible to accurately decode an animal's location. The precision of this decoding sets a lower bound for the amount of information that th...
Main Authors: | Ardi Tampuu, Tambet Matiisen, H Freyja Ólafsdóttir, Caswell Barry, Raul Vicente |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2019-02-01
|
Series: | PLoS Computational Biology |
Online Access: | http://europepmc.org/articles/PMC6407788?pdf=render |
Similar Items
-
Perspective Taking in Deep Reinforcement Learning Agents
by: Aqeel Labash, et al.
Published: (2020-07-01) -
Multiagent cooperation and competition with deep reinforcement learning.
by: Ardi Tampuu, et al.
Published: (2017-01-01) -
LiDAR-as-Camera for End-to-End Driving
by: Ardi Tampuu, et al.
Published: (2023-03-01) -
Do Deep Reinforcement Learning Agents Model Intentions?
by: Tambet Matiisen, et al.
Published: (2022-12-01) -
Hippocampal place cells construct reward related sequences through unexplored space
by: H Freyja Ólafsdóttir, et al.
Published: (2015-06-01)