Minute-encoding neurons in hippocampal-striatal circuits

Shikano, Yu; Ikegaya, Yuji; Sasaki, Takuya

doi:10.1016/j.cub.2021.01.032

Cited by 23 publications

(30 citation statements)

References 56 publications

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“…We speculate that such receptive-field re-scaling also underlies the learning discussed here, which likely builds both on local and network-wide re-weighting of functional connections between neurons and entire regions. Consistent with this idea and the present results, receptive-field re-scaling can occur on a trial-by-trial basis in the striatum (Mello et al, 2015;Gouvêa et al, 2015;Wang et al, 2018) and in the hippocampus (Shikano et al, 2021;Shimbo et al, 2021).…”

Section: The Role Of Feedback In Timed Motor Actionsupporting

confidence: 92%

“…Intriguingly, however, the mechanisms at play may build on similar temporal coding principles as those discussed for motor timing (Yin & Troger, 2011;Eichenbaum, 2014;Howard, 2017;Palombo & Verfaellie, 2017;Nobre & van Ede, 2018;Paton & Buonomano, 2018;J. L. Bellmund et al, 2020; J. L. S. Bellmund et al, 2021;Shikano et al, 2021;Shimbo et al, 2021).…”

Section: Spatiotemporal Coding In the Hippocampus And Striatummentioning

confidence: 97%

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Rapid encoding of task regularities in the human hippocampus guides sensorimotor timing

Polti

Nau

Kaplan

et al. 2021

Preprint

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The brain encodes the statistical regularities of the environment in a task-specific yet flexible and generalizable format. How it does so remains poorly understood. Here, we seek to understand this by converging two parallel lines of research, one centered on striatal-dependent sensorimotor timing, and the other on hippocampal-dependent cognitive mapping. We combined functional magnetic resonance imaging (fMRI) with a visual-tracking and time-to-contact (TTC) estimation task, revealing the widespread brain network supporting sensorimotor learning in real-time. Hippocampal and caudate activity signaled the behavioral feedback within trials and the improvements in performance across trials, suggesting that both structures encode behavior-dependent information rapidly. Critically, hippocampal learning signals generalized across tested intervals, while striatal ones did not, and together they explained both the trial-wise performance and the regression-to-the-mean biases in TTC estimation. Our results suggest that a fundamental function of hippocampal-striatal interactions may be to solve a trade-off between specificity vs. generalization, enabling the flexible and domain-general expression of human timing behavior broadly.

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Section: The Role Of Feedback In Timed Motor Actionsupporting

confidence: 92%

Section: Spatiotemporal Coding In the Hippocampus And Striatummentioning

confidence: 97%

Rapid encoding of task regularities in the human hippocampus guides sensorimotor timing

Polti

Nau

Kaplan

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…It is theoretically important to systematically evaluate whether these regions show the same quantitative distributions; this would require careful experimentation. Because the experimental challenges of estimating time are greatest for times near zero, it may be preferable to conduct this experiment over time scales greater than a few seconds; it has been argued that time cells fire sequentially over several minutes (Shikano, Ikegaya, & Sasaki, 2021; Liu et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Internally Generated Time in the Rodent Hippocampus is Logarithmically Compressed

Curi

Bladon

Charczynski

et al. 2021

Preprint

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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. The analysis at the trial level suggests the time cells represent an internally coherent timeline as a group. Furthermore, even after isolating across-trial variability, time field width increases linearly with delay. Finally, we find that the time cell population is distributed evenly on a logarithmic time scale. Together, these findings provide strong quantitative evidence that the internal neural temporal representation is logarithmically compressed and obeys a neural instantiation of the Weber- Fechner Law.

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“…time cells) [3][4][5][7][8][9] , or the ramping regime [19][20][21][22][23][24] . Given these observations, some studies have shown the coexistence of ramping neurons and sequence neurons in the same neural circuits in the brain 18,21,37 . However, to our knowledge, existing modelling studies of temporal representations have only focused on emulating either the ramping pattern or the sequence pattern within a population of in silico neurons [38][39][40] .…”

Section: Lstm Cellsmentioning

confidence: 99%

Time cell encoding in deep reinforcement learning agents depends on mnemonic demands

Lin

Richards

2021

Preprint

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The representation of "what happened when" is central to encoding episodic and working memories. Recently discovered hippocampal time cells are theorized to provide the neural substrate for such representations by forming distinct sequences that both encode time elapsed and sensory content. However, little work has directly addressed to what extent cognitive demands and temporal structure of experimental tasks affect the emergence and informativeness of these temporal representations. Here, we trained deep reinforcement learning (DRL) agents on a simulated trial-unique nonmatch-to-location (TUNL) task, and analyzed the activities of artificial recurrent units using neuroscience-based methods. We show that, after training, representations resembling both time cells and ramping cells (whose activity increases or decreases monotonically over time) simultaneously emerged in the same population of recurrent units. Furthermore, with simulated variations of the TUNL task that controlled for (1) memory demands during the delay period and (2) the temporal structure of the episodes, we show that memory demands are necessary for the time cells to encode information about the sensory stimuli, while the temporal structure of the task only affected the encoding of "what" and "when" by time cells minimally. Our findings help to reconcile current discrepancies regarding the involvement of time cells in memory-encoding by providing a normative framework. Our modelling results also provide concrete experimental predictions for future studies.

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Minute-encoding neurons in hippocampal-striatal circuits

Cited by 23 publications

References 56 publications

Rapid encoding of task regularities in the human hippocampus guides sensorimotor timing

Rapid encoding of task regularities in the human hippocampus guides sensorimotor timing

Internally Generated Time in the Rodent Hippocampus is Logarithmically Compressed

Time cell encoding in deep reinforcement learning agents depends on mnemonic demands

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