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

Abstract

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.

Cholinergic interneurons in the dorsal striatum play an important role in the acquisition of duration memory

“…In other words, a neural-activity pattern can continuously, not discretely, represent durations of various lengths by changing the transition speed. Therefore, the intermediate peak time can be obtained from the intermediate transition speed (Corte et al, 2022). The above argument was supported by a recent study indicating that the subjective length of duration causally depends on the transition speed of neural-activity pattern in the DS (i.e., slowing the transition of the neural-activity pattern made the rat perceive the duration to be shorter than it was, and vice versa) (Monterio et al, 2023).…”

“…Physiological studies suggested that information on the specific duration, such as a required time, was encoded with the transition of the neuralactivity pattern (Bakuhurin et al, 2017;Matell et al, 2003;Mello et al, 2015;Shikano et al, 2021;Zhou et al, 2020). A previous study argued that "temporal scaling" explains how the transition of the neural-activity pattern induces temporal averaging (Corte et al, 2022). They explain that the original neural-activity pattern encoding the original duration can also encode a longer or shorter duration than the original one by increasing or reducing the transition speed of the original neural-activity pattern (Wang et al, 2018).…”

Cholinergic interneurons in the dorsal striatum play an important role in the acquisition of duration memory

“…Physiological studies suggested that information regarding a specific duration, such as the required time, is encoded by the transition of neural activity patterns (Bakhurin et al, 2017; Mello et al, 2015; Shikano et al, 2021; Zhou et al, 2020). A previous study argued that temporal scaling explains how the transition of neural activity patterns induces temporal averaging (De Corte et al, 2022). They explained that the original neural activity pattern encoding the original duration could also encode a longer or shorter duration than the original by reducing or increasing the transition speed of the original neural activity pattern (Wang et al, 2018).…”

“…In other words, a neural activity pattern can continuously represent durations of various lengths by changing the transition speed. Therefore, the intermediate peak time can be obtained from the intermediate transition speed (De Corte et al, 2022). The above argument is supported by a recent study indicating that the subjective duration causally depends on the transition speed of the neural activity pattern in the DS (i.e.…”