The dorsal hippocampus’ role in context-based timing in rodents

Corte, Benjamin J. De; Farley, Sean J.; Heslin, Kelsey A.; Parker, Krystal L.; Freeman, John H.

doi:10.1016/j.nlm.2022.107673

Cited by 3 publications

(2 citation statements)

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“…The prominent role of hippocampal-associated networks was evident in their divergence across groups of mice that adopted different behavioural strategies during learning as well as in groups of mice that showed evidence of more or less reliance on the formation of temporal expectations to optimize motor behaviour. This is in line with previously described functions of the hippocampus in temporal processing as well as related sensory-driven representations (De Corte et al, 2022; Dylda & Pakan, 2021; Finnie et al, 2021; Pastalkova et al, 2008; Taxidis et al, 2020; Terada et al, 2017). Interestingly, here we show that pre-existing differences in BOLD responses and functional connectivity in hippocampal networks are strongly related to a later capacity for attending to the temporal structure of the task.…”

Section: Discussionsupporting

confidence: 92%

Neural networks representing temporal expectation in mice

Wendlandt,

Wenk,

Henschke

et al. 2024

Preprint

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The ability to attend to specific moments in time is crucial for survival across species facilitating perception and motor performance by leveraging prior temporal knowledge for predictive processing. Despite its importance, the neural mechanisms underlying the utilization of macro-scale and meso-scale neural resources during temporal processing and their relationship to behavioural strategies and motor responses remain largely unexplored. To investigate the capacity for predictive temporal structure of multisensory stimuli to optimize motor behaviour, we established a behavioural paradigm, in which mice were trained to an auditory-cue and visual-target presented at expected or unexpected temporal delays. Using a combination of stimulus-evoked and resting-state functional magnetic resonance imaging, we examined task-related evoked activity in brain-wide networks and found that that the formation of temporal expectations relying on accumulated sensory information and combined multisensory input involves plasticity across large macro-scale cortical networks comprised of primary sensory systems, sensory association areas including posterior parietal cortex, retrosplenial cortex, prefrontal top-down executive control centres of the brain, as well as hippocampal networks. Additionally, employing in vivo two-photon calcium imaging, we explored local single-cell dynamics within the posterior parietal cortex during this task and found that temporal expectation could be decoded directly from neuronal activity within this brain region. Overall, our study provides insights into the neural correlates underlying the formation of multisensory temporal expectations in the mouse brain and highlights the recruitment of neural resources across temporally-driven statistical learning processes.

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Section: Discussionsupporting

confidence: 92%

Neural networks representing temporal expectation in mice

Wendlandt,

Wenk,

Henschke

et al. 2024

Preprint

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“…Averaging-related effects are not the only instances where information is flexibly integrated during timing tasks (for excellent reviews see: Molet and Miller, 2014 ; Gür et al, 2018 ). For example, in a recent series of experiments, we showed that rats expect the intervals associated with distinct cues will covary with one another (De Corte et al, 2018 , 2022 ). In other words, when one cue’s interval changes, they expect other cues’ intervals to have changed in the same direction and can even flexibly update this covariance expectation based on task evidence.…”

Section: Evidence For Population-clock and Ramping Models: The Import...mentioning

confidence: 94%

Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears?

Corte

Akdoğan

Balsam

2022

Front. Behav. Neurosci.

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Timing underlies a variety of functions, from walking to perceiving causality. Neural timing models typically fall into one of two categories—“ramping” and “population-clock” theories. According to ramping models, individual neurons track time by gradually increasing or decreasing their activity as an event approaches. To time different intervals, ramping neurons adjust their slopes, ramping steeply for short intervals and vice versa. In contrast, according to “population-clock” models, multiple neurons track time as a group, and each neuron can fire nonlinearly. As each neuron changes its rate at each point in time, a distinct pattern of activity emerges across the population. To time different intervals, the brain learns the population patterns that coincide with key events. Both model categories have empirical support. However, they often differ in plausibility when applied to certain behavioral effects. Specifically, behavioral data indicate that the timing system has a rich computational capacity, allowing observers to spontaneously compute novel intervals from previously learned ones. In population-clock theories, population patterns map to time arbitrarily, making it difficult to explain how different patterns can be computationally combined. Ramping models are viewed as more plausible, assuming upstream circuits can set the slope of ramping neurons according to a given computation. Critically, recent studies suggest that neurons with nonlinear firing profiles often scale to time different intervals—compressing for shorter intervals and stretching for longer ones. This “temporal scaling” effect has led to a hybrid-theory where, like a population-clock model, population patterns encode time, yet like a ramping neuron adjusting its slope, the speed of each neuron’s firing adapts to different intervals. Here, we argue that these “relative” population-clock models are as computationally plausible as ramping theories, viewing population-speed and ramp-slope adjustments as equivalent. Therefore, we view identifying these “speed-control” circuits as a key direction for evaluating how the timing system performs computations. Furthermore, temporal scaling highlights that a key distinction between different neural models is whether they propose an absolute or relative time-representation. However, we note that several behavioral studies suggest the brain processes both scales, cautioning against a dichotomy.

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