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Transient boosting of action potential backpropagation for few-shot temporal pattern learning.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Transient boosting of action potential backpropagation for few-shot temporal pattern learning.
Συγγραφείς: Sivori G; Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan., Fukai T; Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan.
Πηγή: PLoS computational biology [PLoS Comput Biol] 2025 Dec 05; Vol. 21 (12), pp. e1013777. Date of Electronic Publication: 2025 Dec 05 (Print Publication: 2025).
Τύπος έκδοσης: Journal Article; Research Support, Non-U.S. Gov't
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
Imprint Name(s): Original Publication: San Francisco, CA : Public Library of Science, [2005]-
Ιατρικοί όροι (MeSH): Learning*/physiology , Neurons*/physiology , Models, Neurological* , Action Potentials* , Neuronal Plasticity*, Time Perception/physiology ; Nerve Net/physiology ; Cerebral Cortex/physiology ; Receptors, N-Methyl-D-Aspartate/metabolism ; Animals ; Machine Learning ; Pattern Recognition, Physiological ; Signal-To-Noise Ratio
Περίληψη: One of the remarkable properties of cortical neurons is their innate ability to detect spike patterns in continuous but noisy information streams. As animals learn behaviorally relevant information quickly, pattern detection by neurons should also be rapid. However, how pattern-selective neuronal responses rapidly develop, tune, and remain robust has not been fully understood. Here, we propose a biologically plausible synaptic plasticity rule to learn patterned synaptic inputs rapidly. Our rule facilitates intracellular "self-supervised" learning of intermittently and repeatedly co-activated presynaptic-neuron communities, as in a machine-learning-based rule derived previously for temporal pattern segmentation. Importantly, this model proposes that a spike-triggered transient increase in somatodendritic coupling dramatically boosts the crediting of synapses responsible for the learned response. This boosting effect is essential for rapid pattern learning of single neurons at a high signal-to-noise ratio. Moreover, we demonstrate in recurrent networks how the rule recruits pre-configured cell assemblies for even faster, few-shot learning of multiple patterns. Our results illuminate the self-supervisory role of backpropagating action potentials for rapid pattern learning.
(Copyright: © 2025 Sivori, Fukai. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Competing Interests: The authors have declared that no competing interests exist.
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Grant Information: Japan Society for the Promotion of Science
Substance Nomenclature: 0 (Receptors, N-Methyl-D-Aspartate)
Entry Date(s): Date Created: 20251205 Date Completed: 20251217 Latest Revision: 20260825
Update Code: 20260826
PubMed Central ID: PMC12698000
DOI: 10.1371/journal.pcbi.1013777
PMID: 41348930
Βάση Δεδομένων: MEDLINE
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  Data: Transient boosting of action potential backpropagation for few-shot temporal pattern learning.
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  Data: <searchLink fieldCode="AU" term="%22Sivori+G%22">Sivori G</searchLink>; Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan.<br /><searchLink fieldCode="AU" term="%22Fukai+T%22">Fukai T</searchLink>; Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan.
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  Data: <searchLink fieldCode="JN" term="%22101238922%22">PLoS computational biology</searchLink> [PLoS Comput Biol] 2025 Dec 05; Vol. 21 (12), pp. e1013777. <i>Date of Electronic Publication: </i>2025 Dec 05 (<i>Print Publication: </i>2025).
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  Data: Journal Article; Research Support, Non-U.S. Gov't
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  Data: <i>Original Publication</i>: San Francisco, CA : Public Library of Science, [2005]-
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  Data: <searchLink fieldCode="MM" term="%22Learning%22">Learning*</searchLink>/<searchLink fieldCode="MM" term="%22Learning+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Neurons%22">Neurons*</searchLink>/<searchLink fieldCode="MM" term="%22Neurons+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Models%2C+Neurological%22">Models, Neurological*</searchLink> <br /><searchLink fieldCode="MM" term="%22Action+Potentials%22">Action Potentials*</searchLink> <br /><searchLink fieldCode="MM" term="%22Neuronal+Plasticity%22">Neuronal Plasticity*</searchLink><br /><searchLink fieldCode="MH" term="%22Time+Perception%22">Time Perception</searchLink>/<searchLink fieldCode="MH" term="%22Time+Perception+physiology%22">physiology</searchLink> ; <searchLink fieldCode="MH" term="%22Nerve+Net%22">Nerve Net</searchLink>/<searchLink fieldCode="MH" term="%22Nerve+Net+physiology%22">physiology</searchLink> ; <searchLink fieldCode="MH" term="%22Cerebral+Cortex%22">Cerebral Cortex</searchLink>/<searchLink fieldCode="MH" term="%22Cerebral+Cortex+physiology%22">physiology</searchLink> ; <searchLink fieldCode="MH" term="%22Receptors%2C+N-Methyl-D-Aspartate%22">Receptors, N-Methyl-D-Aspartate</searchLink>/<searchLink fieldCode="MH" term="%22Receptors%2C+N-Methyl-D-Aspartate+metabolism%22">metabolism</searchLink> ; <searchLink fieldCode="MH" term="%22Animals%22">Animals</searchLink> ; <searchLink fieldCode="MH" term="%22Machine+Learning%22">Machine Learning</searchLink> ; <searchLink fieldCode="MH" term="%22Pattern+Recognition%2C+Physiological%22">Pattern Recognition, Physiological</searchLink> ; <searchLink fieldCode="MH" term="%22Signal-To-Noise+Ratio%22">Signal-To-Noise Ratio</searchLink>
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  Label: Abstract
  Group: Ab
  Data: One of the remarkable properties of cortical neurons is their innate ability to detect spike patterns in continuous but noisy information streams. As animals learn behaviorally relevant information quickly, pattern detection by neurons should also be rapid. However, how pattern-selective neuronal responses rapidly develop, tune, and remain robust has not been fully understood. Here, we propose a biologically plausible synaptic plasticity rule to learn patterned synaptic inputs rapidly. Our rule facilitates intracellular "self-supervised" learning of intermittently and repeatedly co-activated presynaptic-neuron communities, as in a machine-learning-based rule derived previously for temporal pattern segmentation. Importantly, this model proposes that a spike-triggered transient increase in somatodendritic coupling dramatically boosts the crediting of synapses responsible for the learned response. This boosting effect is essential for rapid pattern learning of single neurons at a high signal-to-noise ratio. Moreover, we demonstrate in recurrent networks how the rule recruits pre-configured cell assemblies for even faster, few-shot learning of multiple patterns. Our results illuminate the self-supervisory role of backpropagating action potentials for rapid pattern learning.<br /> (Copyright: © 2025 Sivori, Fukai. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
– Name: Abstract
  Label: Competing Interests
  Group: Ab
  Data: The authors have declared that no competing interests exist.
– Name: Ref
  Label: References
  Group: RefInfo
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        Text: English
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        StartPage: e1013777
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      – SubjectFull: Time Perception physiology
        Type: general
      – SubjectFull: Nerve Net physiology
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      – SubjectFull: Cerebral Cortex physiology
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      – SubjectFull: Machine Learning
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      – SubjectFull: Pattern Recognition, Physiological
        Type: general
      – SubjectFull: Signal-To-Noise Ratio
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      – SubjectFull: Learning physiology
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      – SubjectFull: Action Potentials
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      – SubjectFull: Neuronal Plasticity
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      – TitleFull: Transient boosting of action potential backpropagation for few-shot temporal pattern learning.
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              Text: 2025 Dec 05
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            – TitleFull: PLoS computational biology
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