Multisensory integration in chaotic networks.
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| Title: | Multisensory integration in chaotic networks. |
|---|---|
| Authors: | Ponzi, Adam1 (AUTHOR) a.ponzi@ucl.ac.uk, Suzuki, Keisuke1 (AUTHOR) |
| Source: | Neural Networks. Nov2025, Vol. 191, pN.PAG-N.PAG. 1p. |
| Subjects: | Sensory perception, Space perception, Sensorimotor integration, Experimental design, Causal inference, Neural pathways |
| Abstract: | Empirical studies of multisensory spatial perception have uncovered a puzzling array of findings. Illusions, such as the rubber-hand and ventriloquism, demonstrate that simultaneous but spatially separated multisensory stimuli are combined into a single unified percept, but only if they are not too far apart. Intriguingly, the perception of unity fluctuates strongly across apparently identical trials. Spatial localization belief also shows strong fluctuations across identical trials which increase with true spatial disparity, and are larger when beliefs are segregated. Fluctuations are much larger than can be accounted for by external sensory noise sources and also strongly depend on the sequence of preceding stimuli. Here we present a very general and minimal deterministic firing rate network model to explore how fluctuations in spatial localization belief – and the perception of whether these beliefs arise from a single cause – are influenced by the chaotic dynamics of a multisensory brain network. Our study examines the conditions under which these endogenous fluctuations emerge and how they contribute to the unified or segregated nature of perceptual experiences. Crucially, we find that multiple empirical effects observed in multisensory integration arise naturally when the network operates at the edge of chaos. We propose a new neuronal mechanism that estimates the probability of perceiving a unified cause which reflects the extent of network chaos. Additionally, we investigate the effects of varying visual reliability through visual blur and demonstrate that increasing visual blur enhances network chaos, thereby influencing the stability of unified and segregated perceptual states. Ultimately, we calculate explicit proprioceptive and visual beliefs by integrating the emergent internal spatial belief, the unity report probability, and sensory inputs, consistent with Bayesian Causal Inference. The model reproduces a large set of experimental findings, including negative bias in the less reliable sensory modality, increasing fluctuations at low disparity in segregated percepts, and the dependence of belief fluctuations on the sequence of previous stimuli. It makes several novel predictions and provides insights into the role of intrinsic network dynamics in shaping multisensory perception. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Networks is the property of Pergamon Press - An Imprint of Elsevier Science 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.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 187702924 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multisensory integration in chaotic networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ponzi%2C+Adam%22">Ponzi, Adam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> a.ponzi@ucl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Suzuki%2C+Keisuke%22">Suzuki, Keisuke</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Networks%22">Neural Networks</searchLink>. Nov2025, Vol. 191, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Sensory+perception%22">Sensory perception</searchLink><br /><searchLink fieldCode="DE" term="%22Space+perception%22">Space perception</searchLink><br /><searchLink fieldCode="DE" term="%22Sensorimotor+integration%22">Sensorimotor integration</searchLink><br /><searchLink fieldCode="DE" term="%22Experimental+design%22">Experimental design</searchLink><br /><searchLink fieldCode="DE" term="%22Causal+inference%22">Causal inference</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+pathways%22">Neural pathways</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Empirical studies of multisensory spatial perception have uncovered a puzzling array of findings. Illusions, such as the rubber-hand and ventriloquism, demonstrate that simultaneous but spatially separated multisensory stimuli are combined into a single unified percept, but only if they are not too far apart. Intriguingly, the perception of unity fluctuates strongly across apparently identical trials. Spatial localization belief also shows strong fluctuations across identical trials which increase with true spatial disparity, and are larger when beliefs are segregated. Fluctuations are much larger than can be accounted for by external sensory noise sources and also strongly depend on the sequence of preceding stimuli. Here we present a very general and minimal deterministic firing rate network model to explore how fluctuations in spatial localization belief – and the perception of whether these beliefs arise from a single cause – are influenced by the chaotic dynamics of a multisensory brain network. Our study examines the conditions under which these endogenous fluctuations emerge and how they contribute to the unified or segregated nature of perceptual experiences. Crucially, we find that multiple empirical effects observed in multisensory integration arise naturally when the network operates at the edge of chaos. We propose a new neuronal mechanism that estimates the probability of perceiving a unified cause which reflects the extent of network chaos. Additionally, we investigate the effects of varying visual reliability through visual blur and demonstrate that increasing visual blur enhances network chaos, thereby influencing the stability of unified and segregated perceptual states. Ultimately, we calculate explicit proprioceptive and visual beliefs by integrating the emergent internal spatial belief, the unity report probability, and sensory inputs, consistent with Bayesian Causal Inference. The model reproduces a large set of experimental findings, including negative bias in the less reliable sensory modality, increasing fluctuations at low disparity in segregated percepts, and the dependence of belief fluctuations on the sequence of previous stimuli. It makes several novel predictions and provides insights into the role of intrinsic network dynamics in shaping multisensory perception. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Networks is the property of Pergamon Press - An Imprint of Elsevier Science 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: 10.1016/j.neunet.2025.107766 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Sensory perception Type: general – SubjectFull: Space perception Type: general – SubjectFull: Sensorimotor integration Type: general – SubjectFull: Experimental design Type: general – SubjectFull: Causal inference Type: general – SubjectFull: Neural pathways Type: general Titles: – TitleFull: Multisensory integration in chaotic networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ponzi, Adam – PersonEntity: Name: NameFull: Suzuki, Keisuke IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 08936080 Numbering: – Type: volume Value: 191 Titles: – TitleFull: Neural Networks Type: main |
| ResultId | 1 |
