Coupling BCM and neural fields for the emergence of self-organization consensus.
Identifieur interne : 002555 ( Main/Exploration ); précédent : 002554; suivant : 002556Coupling BCM and neural fields for the emergence of self-organization consensus.
Auteurs : Mathieu Lefort [France] ; Yann Boniface ; Bernard GirauSource :
- Advances in experimental medicine and biology [ 0065-2598 ] ; 2011.
Descripteurs français
- KwdFr :
- MESH :
- physiologie : Neurones.
- Apprentissage, Humains, Modèles théoriques.
English descriptors
- KwdEn :
- MESH :
- physiology : Neurons.
- Humans, Learning, Models, Theoretical.
Abstract
Human beings interact with the environment through different modalities, i.e. perceptions and actions, processed in the cortex by dedicated brain areas. These areas are self-organized, so that spatially close neurons are sensitive to close stimuli, providing generalization from previously learned examples. Although perceptive flows are picked up by different spatially separated sensors, their processings are not isolated. On the contrary, they are constantly interacting, as illustrated by the McGurk effect. When the auditory stimulus /ba/ and the /ga/ lip movement are presented simultaneously, people perceive a /da/, which does not correspond to any of the stimuli. Merging several stimuli into one multimodal perception reduces ambiguities and noises and is essential to interact with the environment. This article proposes a model for modality association, inspired by the biological properties of the cortex. The model consists of modality maps interacting through an associative map to raise a consistent multimodal perception of the environment. We propose the coupling of BCM learning rule and neural maps to obtain the decentralized and unsupervised self-organization of each modal map influenced by the multisensory context. We obtain local self-organization of modal maps with various inputs and discretizations.
DOI: 10.1007/978-1-4614-0164-3_5
PubMed: 21744209
Affiliations:
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Le document en format XML
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These areas are self-organized, so that spatially close neurons are sensitive to close stimuli, providing generalization from previously learned examples. Although perceptive flows are picked up by different spatially separated sensors, their processings are not isolated. On the contrary, they are constantly interacting, as illustrated by the McGurk effect. When the auditory stimulus /ba/ and the /ga/ lip movement are presented simultaneously, people perceive a /da/, which does not correspond to any of the stimuli. Merging several stimuli into one multimodal perception reduces ambiguities and noises and is essential to interact with the environment. This article proposes a model for modality association, inspired by the biological properties of the cortex. The model consists of modality maps interacting through an associative map to raise a consistent multimodal perception of the environment. We propose the coupling of BCM learning rule and neural maps to obtain the decentralized and unsupervised self-organization of each modal map influenced by the multisensory context. We obtain local self-organization of modal maps with various inputs and discretizations.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Vandœuvre-lès-Nancy</li></settlement></list><tree><noCountry><name sortKey="Boniface, Yann" sort="Boniface, Yann" uniqKey="Boniface Y" first="Yann" last="Boniface">Yann Boniface</name><name sortKey="Girau, Bernard" sort="Girau, Bernard" uniqKey="Girau B" first="Bernard" last="Girau">Bernard Girau</name></noCountry><country name="France"><region name="Grand Est"><name sortKey="Lefort, Mathieu" sort="Lefort, Mathieu" uniqKey="Lefort M" first="Mathieu" last="Lefort">Mathieu Lefort</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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