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Modeling the Repetition-Based Recovering of Acoustic and Visual Sources With Dendritic Neurons
https://oist.repo.nii.ac.jp/records/2643
https://oist.repo.nii.ac.jp/records/26436d3d86c2-7145-4163-90e8-ba8557ddda65
名前 / ファイル | ライセンス | アクション |
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fnins-16-855753 (4.1 MB)
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CC BY 4.0
Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/) |
Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2022-05-23 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Modeling the Repetition-Based Recovering of Acoustic and Visual Sources With Dendritic Neurons | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | dendritic neurons | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | spiking neural networks | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | blind source separation | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | sound source repetition | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | spatiotemporal structure | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者(英) |
Dellaferrera, Giorgia
× Dellaferrera, Giorgia× Asabuki, Toshitake× Fukai, Tomoki |
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書誌情報 |
en : Frontiers in Neuroscience 巻 16, p. 855753, 発行日 2022-04-28 |
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抄録 | ||||||
内容記述タイプ | Other | |||||
内容記述 | In natural auditory environments, acoustic signals originate from the temporal superimposition of different sound sources. The problem of inferring individual sources from ambiguous mixtures of sounds is known as blind source decomposition. Experiments on humans have demonstrated that the auditory system can identify sound sources as repeating patterns embedded in the acoustic input. Source repetition produces temporal regularities that can be detected and used for segregation. Specifically, listeners can identify sounds occurring more than once across different mixtures, but not sounds heard only in a single mixture. However, whether such a behavior can be computationally modeled has not yet been explored. Here, we propose a biologically inspired computational model to perform blind source separation on sequences of mixtures of acoustic stimuli. Our method relies on a somatodendritic neuron model trained with a Hebbian-like learning rule which was originally conceived to detect spatio-temporal patterns recurring in synaptic inputs. We show that the segregation capabilities of our model are reminiscent of the features of human performance in a variety of experimental settings involving synthesized sounds with naturalistic properties. Furthermore, we extend the study to investigate the properties of segregation on task settings not yet explored with human subjects, namely natural sounds and images. Overall, our work suggests that somatodendritic neuron models offer a promising neuro-inspired learning strategy to account for the characteristics of the brain segregation capabilities as well as to make predictions on yet untested experimental settings. | |||||
出版者 | ||||||
出版者 | Frontiers Media | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1662-453X | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1662-4548 | |||||
PubMed番号 | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | PMID | |||||
関連識別子 | info:pmid/35573290 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | info:doi/10.3389/fnins.2022.855753 | |||||
権利 | ||||||
権利情報 | © 2022 Dellaferrera, Asabuki and Fukai. | |||||
関連サイト | ||||||
識別子タイプ | URI | |||||
関連識別子 | https://www.frontiersin.org/articles/10.3389/fnins.2022.855753/full | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 |