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An Oscillator Ensemble Model of Sequence Learning

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An Oscillator Ensemble Model of Sequence Learning. / Maye, Alexander; Wang, Peng; Daume, Jonathan; Hu, Xiaolin; Engel, Andreas K.

In: FRONT INTEGR NEUROSC, Vol. 13, 20.08.2019, p. 43.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Maye, A, Wang, P, Daume, J, Hu, X & Engel, AK 2019, 'An Oscillator Ensemble Model of Sequence Learning', FRONT INTEGR NEUROSC, vol. 13, pp. 43. https://doi.org/10.3389/fnint.2019.00043

APA

Maye, A., Wang, P., Daume, J., Hu, X., & Engel, A. K. (2019). An Oscillator Ensemble Model of Sequence Learning. FRONT INTEGR NEUROSC, 13, 43. https://doi.org/10.3389/fnint.2019.00043

Vancouver

Maye A, Wang P, Daume J, Hu X, Engel AK. An Oscillator Ensemble Model of Sequence Learning. FRONT INTEGR NEUROSC. 2019 Aug 20;13:43. https://doi.org/10.3389/fnint.2019.00043

Bibtex

@article{31339bd6eb26474fb782752f830107a8,
title = "An Oscillator Ensemble Model of Sequence Learning",
abstract = "Learning and memorizing sequences of events is an important function of the human brain and the basis for forming expectations and making predictions. Learning is facilitated by repeating a sequence several times, causing rhythmic appearance of the individual sequence elements. This observation invites to consider the resulting multitude of rhythms as a spectral {"}fingerprint{"} which characterizes the respective sequence. Here we explore the implications of this perspective by developing a neurobiologically plausible computational model which captures this {"}fingerprint{"} by attuning an ensemble of neural oscillators. In our model, this attuning process is based on a number of oscillatory phenomena that have been observed in electrophysiological recordings of brain activity like synchronization, phase locking, and reset as well as cross-frequency coupling. We compare the learning properties of the model with behavioral results from a study in human participants and observe good agreement of the errors for different levels of complexity of the sequence to be memorized. Finally, we suggest an extension of the model for processing sequences that extend over several sensory modalities.",
author = "Alexander Maye and Peng Wang and Jonathan Daume and Xiaolin Hu and Engel, {Andreas K}",
year = "2019",
month = aug,
day = "20",
doi = "10.3389/fnint.2019.00043",
language = "English",
volume = "13",
pages = "43",
journal = "FRONT INTEGR NEUROSC",
issn = "1662-5145",
publisher = "Frontiers Media S. A.",

}

RIS

TY - JOUR

T1 - An Oscillator Ensemble Model of Sequence Learning

AU - Maye, Alexander

AU - Wang, Peng

AU - Daume, Jonathan

AU - Hu, Xiaolin

AU - Engel, Andreas K

PY - 2019/8/20

Y1 - 2019/8/20

N2 - Learning and memorizing sequences of events is an important function of the human brain and the basis for forming expectations and making predictions. Learning is facilitated by repeating a sequence several times, causing rhythmic appearance of the individual sequence elements. This observation invites to consider the resulting multitude of rhythms as a spectral "fingerprint" which characterizes the respective sequence. Here we explore the implications of this perspective by developing a neurobiologically plausible computational model which captures this "fingerprint" by attuning an ensemble of neural oscillators. In our model, this attuning process is based on a number of oscillatory phenomena that have been observed in electrophysiological recordings of brain activity like synchronization, phase locking, and reset as well as cross-frequency coupling. We compare the learning properties of the model with behavioral results from a study in human participants and observe good agreement of the errors for different levels of complexity of the sequence to be memorized. Finally, we suggest an extension of the model for processing sequences that extend over several sensory modalities.

AB - Learning and memorizing sequences of events is an important function of the human brain and the basis for forming expectations and making predictions. Learning is facilitated by repeating a sequence several times, causing rhythmic appearance of the individual sequence elements. This observation invites to consider the resulting multitude of rhythms as a spectral "fingerprint" which characterizes the respective sequence. Here we explore the implications of this perspective by developing a neurobiologically plausible computational model which captures this "fingerprint" by attuning an ensemble of neural oscillators. In our model, this attuning process is based on a number of oscillatory phenomena that have been observed in electrophysiological recordings of brain activity like synchronization, phase locking, and reset as well as cross-frequency coupling. We compare the learning properties of the model with behavioral results from a study in human participants and observe good agreement of the errors for different levels of complexity of the sequence to be memorized. Finally, we suggest an extension of the model for processing sequences that extend over several sensory modalities.

U2 - 10.3389/fnint.2019.00043

DO - 10.3389/fnint.2019.00043

M3 - SCORING: Journal article

C2 - 31481883

VL - 13

SP - 43

JO - FRONT INTEGR NEUROSC

JF - FRONT INTEGR NEUROSC

SN - 1662-5145

ER -