Internally generated time in the rodent hippocampus is logarithmically compressed

  1. Rui Cao
  2. John H Bladon
  3. Stephen J Charczynski
  4. Michael E Hasselmo
  5. Marc W Howard

  • Curated by eLife

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    eLife Assessment:

    This is a rigorous evaluation of whether the compression of time cells in the hippocampus follows the Weber-Fechner Law, using a hierarchical Bayesian model that simultaneously accounts for the firing pattern at the trial, cell, and population levels. The two key results are that the time field width increases linearly with delay, even after taking into account the across trial response variability, and that the time cell population is distributed evenly on a logarithmic time scale. Overall, the paper is well written, the experiment and data analysis are technically sound, and the conclusions are mostly well supported.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

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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.

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  1. Version published to 10.7554/elife.75353 on eLife
  2. Version published to 10.1101/2021.10.25.465750v2 on bioRxiv
  3. eLife

    eLife Assessment:

    This is a rigorous evaluation of whether the compression of time cells in the hippocampus follows the Weber-Fechner Law, using a hierarchical Bayesian model that simultaneously accounts for the firing pattern at the trial, cell, and population levels. The two key results are that the time field width increases linearly with delay, even after taking into account the across trial response variability, and that the time cell population is distributed evenly on a logarithmic time scale. Overall, the paper is well written, the experiment and data analysis are technically sound, and the conclusions are mostly well supported.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

  4. eLife

    Joint Public Review:

    This study investigates whether the compression of time cells in the hippocampus follows the Weber-Fechner Law, using a hierarchical Bayesian model that simultaneously accounts for the firing pattern at the trial, cell, and population levels. Recording where performed in dorsal CA1 of rats (N = 4) performing a delayed paired-associate task that required rats to run on a treadmill between sample and test, during which time delay-dependent firing could be assessed (N = 131 neurons). The authors highlight three novel observations: 1) Simultaneously recorded CA1 neurons showed consistent deviations in their trial-to-trial variability in the timing of peak activity, 2) After controlling for trial-to-trial variability, time field width increased linearly with delay, and 3) The number of neurons with time field peaks observed at each delay was logarithmically allocated across the treadmill interval. The findings are related to the authors' broader theory of how hippocampal firing provides a continuous, scale-free temporal context that defines the backbone of episodic memory.

    The findings in this paper are interesting, timely, and the data generally support the conclusions, though some technical revisions would be beneficial. The strong theoretical basis of this line of inquiry from both a physiological and cognitive perspective is appreciated. The hierarchical Bayesian analysis is a major strength of the paper and a relatively novel contribution to the hippocampal field. The main concern about this study is that all the conclusions of the paper are based on the results of the hierarchical Bayesian model, urging for alternative analytical accounts for the increase in time field width as a function of delay. In addition, more can be done to address the observed variability in time field activity and whether behavioral changes explain apparent changes in the receptive field properties for late firing neurons.

  5. Version published to 10.1101/2021.10.25.465750v1 on bioRxiv