Academic Journal
Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive.
| Τίτλος: | Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive. |
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| Συγγραφείς: | Pastorelli, Elena, Yegenoglu, Alper, Kolodziej, Nicole, Wybo, Willem, Simula, Francesco, Diaz-Pier, Sandra, Storm, Johan Frederik, Paolucci, Pier Stanislao |
| Πηγή: | Frontiers in Computational Neuroscience; 2025, p1-20, 20p |
| Θεματικοί όροι: | Machine learning, Pyramidal neurons, Sleep stages, Rapid eye movement sleep, Artificial intelligence |
| Περίληψη: | Mounting experimental evidence suggests the hypothesis that brain-state-specific neural mechanisms, supported by the connectome shaped by evolution, could play a crucial role in integrating past and contextual knowledge with the current, incoming flow of evidence (e.g., from sensory systems). These mechanisms would operate across multiple spatial and temporal scales, necessitating dedicated support at the levels of individual neurons and synapses. A notable feature within the neocortex is the structure of large, deep pyramidal neurons, which exhibit a distinctive separation between an apical dendritic compartment and a basal dendritic/perisomatic compartment. This separation is characterized by distinct patterns of incoming connections and three brain-state-specific activation mechanisms, namely, apical-amplification, -isolation, and drive, which have been proposed to be associated - with wakefulness, deeper NREM sleep stages, and REM sleep, respectively. The cognitive roles of apical mechanisms have been supported by experiments in behaving animals. In contrast, classical models of learning in spiking networks are based on single-compartment neurons, lacking the ability to describe the integration of apical and basal/somatic information. This work provides the computational community with a two-compartment spiking neuron model that supports the proposed forms of brain-state-specific activity. A machine learning evolutionary algorithm, guided by a set of fitness functions, selected parameters defining neurons that express the desired apical dendritic mechanisms. The resulting spiking model can be further approximated by a piece-wise linear transfer function (ThetaPlanes) for use in large-scale bio-inspired artificial intelligence systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Frontiers in Computational Neuroscience is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pastorelli%2C+Elena%22">Pastorelli, Elena</searchLink><br /><searchLink fieldCode="AR" term="%22Yegenoglu%2C+Alper%22">Yegenoglu, Alper</searchLink><br /><searchLink fieldCode="AR" term="%22Kolodziej%2C+Nicole%22">Kolodziej, Nicole</searchLink><br /><searchLink fieldCode="AR" term="%22Wybo%2C+Willem%22">Wybo, Willem</searchLink><br /><searchLink fieldCode="AR" term="%22Simula%2C+Francesco%22">Simula, Francesco</searchLink><br /><searchLink fieldCode="AR" term="%22Diaz-Pier%2C+Sandra%22">Diaz-Pier, Sandra</searchLink><br /><searchLink fieldCode="AR" term="%22Storm%2C+Johan+Frederik%22">Storm, Johan Frederik</searchLink><br /><searchLink fieldCode="AR" term="%22Paolucci%2C+Pier+Stanislao%22">Paolucci, Pier Stanislao</searchLink> – Name: TitleSource Label: Source Group: Src Data: Frontiers in Computational Neuroscience; 2025, p1-20, 20p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Pyramidal+neurons%22">Pyramidal neurons</searchLink><br /><searchLink fieldCode="DE" term="%22Sleep+stages%22">Sleep stages</searchLink><br /><searchLink fieldCode="DE" term="%22Rapid+eye+movement+sleep%22">Rapid eye movement sleep</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Mounting experimental evidence suggests the hypothesis that brain-state-specific neural mechanisms, supported by the connectome shaped by evolution, could play a crucial role in integrating past and contextual knowledge with the current, incoming flow of evidence (e.g., from sensory systems). These mechanisms would operate across multiple spatial and temporal scales, necessitating dedicated support at the levels of individual neurons and synapses. A notable feature within the neocortex is the structure of large, deep pyramidal neurons, which exhibit a distinctive separation between an apical dendritic compartment and a basal dendritic/perisomatic compartment. This separation is characterized by distinct patterns of incoming connections and three brain-state-specific activation mechanisms, namely, apical-amplification, -isolation, and drive, which have been proposed to be associated - with wakefulness, deeper NREM sleep stages, and REM sleep, respectively. The cognitive roles of apical mechanisms have been supported by experiments in behaving animals. In contrast, classical models of learning in spiking networks are based on single-compartment neurons, lacking the ability to describe the integration of apical and basal/somatic information. This work provides the computational community with a two-compartment spiking neuron model that supports the proposed forms of brain-state-specific activity. A machine learning evolutionary algorithm, guided by a set of fitness functions, selected parameters defining neurons that express the desired apical dendritic mechanisms. The resulting spiking model can be further approximated by a piece-wise linear transfer function (ThetaPlanes) for use in large-scale bio-inspired artificial intelligence systems. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Frontiers in Computational Neuroscience is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3389/fncom.2025.1566196 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Pyramidal neurons Type: general – SubjectFull: Sleep stages Type: general – SubjectFull: Rapid eye movement sleep Type: general – SubjectFull: Artificial intelligence Type: general Titles: – TitleFull: Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pastorelli, Elena – PersonEntity: Name: NameFull: Yegenoglu, Alper – PersonEntity: Name: NameFull: Kolodziej, Nicole – PersonEntity: Name: NameFull: Wybo, Willem – PersonEntity: Name: NameFull: Simula, Francesco – PersonEntity: Name: NameFull: Diaz-Pier, Sandra – PersonEntity: Name: NameFull: Storm, Johan Frederik – PersonEntity: Name: NameFull: Paolucci, Pier Stanislao IsPartOfRelationships: – BibEntity: Dates: – D: 03 M: 06 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 16625188 Titles: – TitleFull: Frontiers in Computational Neuroscience Type: main |
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