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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Behavioral Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbeh.2022.1022713/full |
| _version_ | 1811301579314167808 |
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| author | Benjamin J. De Corte Benjamin J. De Corte Başak Akdoğan Başak Akdoğan Peter D. Balsam Peter D. Balsam Peter D. Balsam |
| author_facet | Benjamin J. De Corte Benjamin J. De Corte Başak Akdoğan Başak Akdoğan Peter D. Balsam Peter D. Balsam Peter D. Balsam |
| author_sort | Benjamin J. De Corte |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T07:12:30Z |
| format | Article |
| id | doaj.art-6451593e15274845ba77a31e94b357d7 |
| institution | Directory Open Access Journal |
| issn | 1662-5153 |
| language | English |
| last_indexed | 2024-04-13T07:12:30Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Behavioral Neuroscience |
| spelling | doaj.art-6451593e15274845ba77a31e94b357d72022-12-22T02:56:51ZengFrontiers Media S.A.Frontiers in Behavioral Neuroscience1662-51532022-12-011610.3389/fnbeh.2022.10227131022713Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears?Benjamin J. De Corte0Benjamin J. De Corte1Başak Akdoğan2Başak Akdoğan3Peter D. Balsam4Peter D. Balsam5Peter D. Balsam6Department of Psychology, Columbia University, New York, NY, United StatesDivision of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United StatesDepartment of Psychology, Columbia University, New York, NY, United StatesDivision of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United StatesDepartment of Psychology, Columbia University, New York, NY, United StatesDivision of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United StatesDepartment of Neuroscience and Behavior, Barnard College, New York, NY, United StatesTiming 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.https://www.frontiersin.org/articles/10.3389/fnbeh.2022.1022713/fulltime perceptiondrift-diffusionpopulation-clocktemporal scalingtemporal averagingramping activity |
| spellingShingle | Benjamin J. De Corte Benjamin J. De Corte Başak Akdoğan Başak Akdoğan Peter D. Balsam Peter D. Balsam Peter D. Balsam Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears? Frontiers in Behavioral Neuroscience time perception drift-diffusion population-clock temporal scaling temporal averaging ramping activity |
| 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 | time perception drift-diffusion population-clock temporal scaling temporal averaging ramping activity |
| url | https://www.frontiersin.org/articles/10.3389/fnbeh.2022.1022713/full |
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