@tsaoNeuralBasesTiming2022

The neural bases for timing of durations

Read:: - [ ] Tsao et al. (2022) - The neural bases for timing of durations 🛫2023-09-11 !!2 rd citation todoist Print::  ❌ Zotero Link:: Zotero Files:: attachment Reading Note:: Web Rip:: url:: https://www.nature.com/articles/s41583-022-00623-3

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Abstract

Durations are defined by a beginning and an end, and a major distinction is drawn between durations that start in the present and end in the future (‘prospective timing’) and durations that start in the past and end either in the past or the present (‘retrospective timing’). Different psychological processes are thought to be engaged in each of these cases. The former is thought to engage a clock-like mechanism that accurately tracks the continuing passage of time, whereas the latter is thought to engage a reconstructive process that utilizes both temporal and non-temporal information from the memory of past events. We propose that, from a biological perspective, these two forms of duration estimation are supported by computational processes that are both reliant on population state dynamics but are nevertheless distinct. Prospective timing is effectively carried out in a single step where the ongoing dynamics of population activity directly serve as the computation of duration, whereas retrospective timing is carried out in two steps: the initial generation of population state dynamics through the process of event segmentation and the subsequent computation of duration utilizing the memory of those dynamics.

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Further Reading

• 23. Buonomano, D. V. & Laje, R. Population clocks: motor timing with neural dynamics. Trends Cogn. Sci. 14, 520–527 (2010). 24. Buonomano, D. V. & Mauk, M. D. Neural network model of the cerebellum: temporal discrimination and the timing of motor responses. Neural Comput. 6, 38–55 (1994). 25. Remington, E. D., Egger, S. W., Narain, D., Wang, J. & Jazayeri, M. A dynamical systems perspective on flexible motor timing. Trends Cogn. Sci. 22, 938–952 (2018). rd p5 🛫2023-09-11
  • All related to “population Clocks” And seemingly bases this on a PA model related to different neural states

• 43. Wang, J., Narain, D., Hosseini, E. A. & Jazayeri, M. Flexible timing by temporal scaling of cortical responses. Nat. Neurosci. 21, 102–110 (2018). This study demonstrates that motor timing can be carried out by controlling the speed at which neural trajectories evolve, which is reflected in the temporal scaling of single-unit responses. Recurrent neural networks trained to perform the same timing task also reach the same solution for controlling the speed at which population activity evolves. rd p5 🛫2023-09-11
  • wangFlexibleTimingTemporal2018

• 110 . Buonomano, D. V. A learning rule for the emergence of stable dynamics and timing in recurrent networks. J. Neurophysiol. 94, 2275–2283 (2005). rd p5 🛫2023-09-11

• 11 3 . Rajan, K., Harvey, C. D. & Tank, D. W. Recurrent network models of sequence generation and memory. Neuron 90, 128–142 (2016). rd p5 🛫2023-09-11

• 11 4 . Goudar, V. & Buonomano, D. V. Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks. Elife https://doi.org/10.7554/ eLife.31134 (2018 rd p5 🛫2023-09-11

• 11 5 . Bi, Z. & Zhou, C. Understanding the computation of time using neural network models. Proc. Natl Acad. Sci. USA 11 7 , 10530 (2020). rd p5 🛫2023-09-11

• 11 6 . Pérez, O. & Merchant, H. The synaptic properties of cells define the hallmarks of interval timing in a recurrent neural network. J. Neurosci. 38, 4186 (2018). rd p5 🛫2023-09-11

• 11 7 . Hardy, N. F., Goudar, V., Romero-Sosa, J. L. & Buonomano, D. V. A model of temporal scaling correctly predicts that motor timing improves with speed. Nat. Commun. 9, 4732 (2018). rd p5 🛫2023-09-11

• 11 8 . Laje, R. & Buonomano, D. V. Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16, 925–933 (2013). This study demonstrates that recurrent neural networks are able to generate stable trajectories capable of carrying out motor timing. rd p5 🛫2023-09-11

• 11 9 . Goel, A. & Buonomano, D. V. Temporal interval learning in cortical cultures is encoded in intrinsic network dynamics. Neuron 91, 320–327 (2016). rd p5 🛫2023-09-11

• 120. Johnson, H. A., Goel, A. & Buonomano, D. V. Neural dynamics of in vitro cortical networks reflects experienced temporal patterns. Nat. Neurosci. 13, 917–919 (2010). rd p5 🛫2023-09-11

Topics

This type of timing mechanism has sometimes been referred to as a ‘population clock’, a n d it does not necessarily need to generate a linear metrical representation of time23,24. tp

population clock tp

Prospective timing The estimation of an ongoing duration in the present moment. Events The basic units of organization for experience, defined primarily as perceived time intervals whose beginnings and ends are clearly defined. Retrospective timing The estimation of duration based on memory of past events. tp

Population clock The encoding of temporal information through changes in neural population activity over time. Explicit timing Prospective timing in which subjects are aware that they should attend to the passage of time to either estimate a duration defined by external events or generate a timed action. Neural trajectory A sequence of population states over time which describe the evolution of neural population activity. Implicit timing Prospective timing in which no overt timing behaviour is required. tp

Extracted Annotations and Comments

Page 646

Early models proposed that the internal clock would be implemented through a pacemakeraccumulator mechanism11–13 in which pulses generated by a central pacemaker would be integrated by an accumulator to generate estimates of time. However, outside the circadian system14,15, evidence for biological implementation of such a mechanism has been missing

Page 646

Subsequently, it has been proposed that the internal clock is implemented through coincidence detection of oscillatory activity patterns16–18, but, even though changes in oscillatory activity have been correlated to timing behaviour19–21, evidence of coincidence detection has also been missing.

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I don’t get the last statement. Coincidence detection is the entire point of their own citation

Page 646

Instead, time can be encoded through changes in neural population activity over time, or population state dynamics, as long as each moment in physical time corresponds to a unique population activity pattern.

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yes? so?

Page 647

Task performance was also dependent on hippocampal activity57, consistent with a growing number of studies suggesting that for durations of tens of seconds and longer, the hippocampus may play a central role58–61.

Page 647

First, retrospective timing is operationally defined as the estimation of a duration when subjects are unaware beforehand that they will estimate the duration, whereas prospective timing is defined as estimation of duration when subjects are aware beforehand161. The logic of this operational definition is that, particularly during prospective timing, subjects have the opportunity to explicitly track the passage of time. Thus, in the case where estimation occurs after the duration has ended but subjects are already aware beforehand that they will eventually estimate duration, this would still be considered prospective timing.

Page 647

Second, ‘prospective timing’ and ‘retrospective timing’ refer specifically to the estimation of duration, even though timing encompasses temporal order in addition to duration. Whether there exists a similar distinction between temporal order based on ongoing events as opposed to memory of past events is unclear.

Page 647

Finally, ‘prospective timing’ overlaps with other commonly used terms such as ‘interval timing’ and ‘reward timing’, whereas ‘retrospective timing’ partially overlaps with ‘episodic timing’, and each has also been referred to as ‘experienced duration’ and ‘remembered duration’, respectively335.

Page 651

Although local circuits may be capable of generating stable trajectories, experimental observations demonstrate that interareal interactions also play an important role in the generation of stable trajectories.

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GOing to use the word “interareal”

Figures (blue)

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