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

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...

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Autores principales: De Corte, Benjamin J., Akdoğan, Başak, Balsam, Peter D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9773401/
https://www.ncbi.nlm.nih.gov/pubmed/36570701
http://dx.doi.org/10.3389/fnbeh.2022.1022713
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author De Corte, Benjamin J.
Akdoğan, Başak
Balsam, Peter D.
author_facet De Corte, Benjamin J.
Akdoğan, Başak
Balsam, Peter D.
author_sort De Corte, Benjamin J.
collection PubMed
description 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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spelling pubmed-97734012022-12-23 Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears? De Corte, Benjamin J. Akdoğan, Başak Balsam, Peter D. Front Behav Neurosci Behavioral Neuroscience 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. Frontiers Media S.A. 2022-12-08 /pmc/articles/PMC9773401/ /pubmed/36570701 http://dx.doi.org/10.3389/fnbeh.2022.1022713 Text en Copyright © 2022 De Corte, Akdoğan and Balsam. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Behavioral Neuroscience
De Corte, Benjamin J.
Akdoğan, Başak
Balsam, Peter D.
Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears?
title Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears?
title_full Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears?
title_fullStr Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears?
title_full_unstemmed Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears?
title_short Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears?
title_sort temporal scaling and computing time in neural circuits: should we stop watching the clock and look for its gears?
topic Behavioral Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9773401/
https://www.ncbi.nlm.nih.gov/pubmed/36570701
http://dx.doi.org/10.3389/fnbeh.2022.1022713
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