Internally generated time in the rodent hippocampus is logarithmically compressed
Files
Published version
Date
2022-10-17
Authors
Cao, Rui
Bladon, John H.
Charczynski, Stephen J.
Hasselmo, Michael E.
Howard, Marc W.
Version
Published version
OA Version
Citation
R. Cao, J.H. Bladon, S.J. Charczynski, M.E. Hasselmo, M.W. Howard. 2022. "Internally generated time in the rodent hippocampus is logarithmically compressed." eLife, Volume 11, pp.e75353-. https://doi.org/10.7554/eLife.75353
Abstract
The Weber-Fechner law proposes that our perceived sensory input increases with physical input on a logarithmic scale. Hippocampal 'time cells' carry a record of recent experience by firing sequentially during a circumscribed period of time after a triggering stimulus. Different cells have 'time fields' at different delays up to at least tens of seconds. Past studies suggest that time cells represent a compressed timeline by demonstrating that fewer time cells fire late in the delay and their time fields are wider. This paper asks whether the compression of time cells obeys the Weber-Fechner Law. Time cells were studied with a hierarchical Bayesian model that simultaneously accounts for the firing pattern at the trial level, cell level, and population level. This procedure allows separate estimates of the within-trial receptive field width and the across-trial variability. After isolating across-trial variability, time field width increased linearly with delay. Further, the time cell population was distributed evenly along a logarithmic time axis. These findings provide strong quantitative evidence that the neural temporal representation in rodent hippocampus is logarithmically compressed and obeys a neural Weber-Fechner Law.
Description
License
Attribution 4.0 International
© 2022, Cao, Bladon et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
© 2022, Cao, Bladon et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.