Recurrent network for multisensory integration-Identification of common sources of audiovisual stimuli-
We perceive our surrounding environment by using different sense organs. However, it is notclear how the brain estimate information from our surroundings from the multisensory stimuli itreceives. While Bayesian inference provides a normative account of the computational principle atwork in the brain...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-07-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00101/full |
| _version_ | 1818447156934082560 |
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| author | Itsuki eYamashita Kentaro eKatahira Kentaro eKatahira Kentaro eKatahira Yasuhiko eIgarashi Kazuo eOkanoya Kazuo eOkanoya Kazuo eOkanoya Masato eOkada Masato eOkada Masato eOkada |
| author_facet | Itsuki eYamashita Kentaro eKatahira Kentaro eKatahira Kentaro eKatahira Yasuhiko eIgarashi Kazuo eOkanoya Kazuo eOkanoya Kazuo eOkanoya Masato eOkada Masato eOkada Masato eOkada |
| author_sort | Itsuki eYamashita |
| collection | DOAJ |
| description | We perceive our surrounding environment by using different sense organs. However, it is notclear how the brain estimate information from our surroundings from the multisensory stimuli itreceives. While Bayesian inference provides a normative account of the computational principle atwork in the brain, it does not provide information on how the nervous system actually implementsthe computation. To provide an insight into how the neural dynamics are related to multisensoryintegration, we constructed a recurrent network model that can implement computations relatedto multisensory integration. Our model not only extracts information from noisy neural activitypatterns, it also estimates a causal structure; i.e., it can infer whether the different stimuli camefrom the same source or different sources. We show that our model can reproduce the resultsof psychophysical experiments on spatial unity and localization bias which indicate that a shiftoccurs in the perceived position of a stimulus through the effect of another simultaneous stimulus.The experimental data have been reproduced in previous studies using Bayesian models. Bycomparing the Bayesian model and our neural network model, we investigated how the Bayesianprior is represented in neural circuits. |
| first_indexed | 2024-12-14T19:59:09Z |
| format | Article |
| id | doaj.art-bfa16004ac9b4c1fa143955849e175cf |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-14T19:59:09Z |
| publishDate | 2013-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-bfa16004ac9b4c1fa143955849e175cf2022-12-21T22:49:13ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882013-07-01710.3389/fncom.2013.0010151080Recurrent network for multisensory integration-Identification of common sources of audiovisual stimuli-Itsuki eYamashita0Kentaro eKatahira1Kentaro eKatahira2Kentaro eKatahira3Yasuhiko eIgarashi4Kazuo eOkanoya5Kazuo eOkanoya6Kazuo eOkanoya7Masato eOkada8Masato eOkada9Masato eOkada10The University of TokyoThe University of TokyoJapan Science Technology Agency, ERATO, Okanoya Emotional Information ProjectRIKEN Brain Science InstituteThe University of TokyoThe University of TokyoJapan Science Technology Agency, ERATO, Okanoya Emotional Information ProjectRIKEN Brain Science InstituteThe University of TokyoJapan Science Technology Agency, ERATO, Okanoya Emotional Information ProjectRIKEN Brain Science InstituteWe perceive our surrounding environment by using different sense organs. However, it is notclear how the brain estimate information from our surroundings from the multisensory stimuli itreceives. While Bayesian inference provides a normative account of the computational principle atwork in the brain, it does not provide information on how the nervous system actually implementsthe computation. To provide an insight into how the neural dynamics are related to multisensoryintegration, we constructed a recurrent network model that can implement computations relatedto multisensory integration. Our model not only extracts information from noisy neural activitypatterns, it also estimates a causal structure; i.e., it can infer whether the different stimuli camefrom the same source or different sources. We show that our model can reproduce the resultsof psychophysical experiments on spatial unity and localization bias which indicate that a shiftoccurs in the perceived position of a stimulus through the effect of another simultaneous stimulus.The experimental data have been reproduced in previous studies using Bayesian models. Bycomparing the Bayesian model and our neural network model, we investigated how the Bayesianprior is represented in neural circuits.http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00101/fullrecurrent neural networkmultisensory integrationSpatial OrientationCausality inferenceMexican-hat type interaction. |
| spellingShingle | Itsuki eYamashita Kentaro eKatahira Kentaro eKatahira Kentaro eKatahira Yasuhiko eIgarashi Kazuo eOkanoya Kazuo eOkanoya Kazuo eOkanoya Masato eOkada Masato eOkada Masato eOkada Recurrent network for multisensory integration-Identification of common sources of audiovisual stimuli- Frontiers in Computational Neuroscience recurrent neural network multisensory integration Spatial Orientation Causality inference Mexican-hat type interaction. |
| title | Recurrent network for multisensory integration-Identification of common sources of audiovisual stimuli- |
| title_full | Recurrent network for multisensory integration-Identification of common sources of audiovisual stimuli- |
| title_fullStr | Recurrent network for multisensory integration-Identification of common sources of audiovisual stimuli- |
| title_full_unstemmed | Recurrent network for multisensory integration-Identification of common sources of audiovisual stimuli- |
| title_short | Recurrent network for multisensory integration-Identification of common sources of audiovisual stimuli- |
| title_sort | recurrent network for multisensory integration identification of common sources of audiovisual stimuli |
| topic | recurrent neural network multisensory integration Spatial Orientation Causality inference Mexican-hat type interaction. |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00101/full |
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