Learning to predict future locations with internally generated theta sequences
Parra-Barrero, E., &
Cheng, S.PLOS Computational Biology,
19(5), e1011101
@article{Parra-BarreroCheng2023,
author = {Parra-Barrero, Eloy and Cheng, Sen},
title = {Learning to predict future locations with internally generated theta sequences},
journal = {PLOS Computational Biology},
volume = {19},
number = {5},
pages = {e1011101},
month = {May},
year = {2023},
doi = {10.1371/journal.pcbi.1011101},
}
2021
Neuronal Sequences during Theta Rely on Behavior-Dependent Spatial Maps
Parra-Barrero, E., Diba, K., &
Cheng, S.
@article{Parra-BarreroDibaCheng2021,
author = {Parra-Barrero, Eloy and Diba, Kamran and Cheng, Sen},
title = {Neuronal Sequences during Theta Rely on Behavior-Dependent Spatial Maps},
journal = {eLife},
volume = {10},
pages = {e70296},
year = {2021},
doi = {10.7554/eLife.70296},
}
A large number of cell types in the mammalian brain code for various types of spatial information, e.g., head direction cells, place cells, and grid cells. We study how networks of these cell types could support spatial navigation by combining computational modeling and data analysis.