Pooling strategies in V1 can account for the functional and structural diversity across species.
Neurons in the primary visual cortex are selective to orientation with various degrees of selectivity to the spatial phase, from high selectivity in simple cells to low selectivity in complex cells. Various computational models have suggested a possible link between the presence of phase invariant c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-07-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010270 |
| _version_ | 1818486778182500352 |
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| author | Victor Boutin Angelo Franciosini Frédéric Chavane Laurent U Perrinet |
| author_facet | Victor Boutin Angelo Franciosini Frédéric Chavane Laurent U Perrinet |
| author_sort | Victor Boutin |
| collection | DOAJ |
| description | Neurons in the primary visual cortex are selective to orientation with various degrees of selectivity to the spatial phase, from high selectivity in simple cells to low selectivity in complex cells. Various computational models have suggested a possible link between the presence of phase invariant cells and the existence of orientation maps in higher mammals' V1. These models, however, do not explain the emergence of complex cells in animals that do not show orientation maps. In this study, we build a theoretical model based on a convolutional network called Sparse Deep Predictive Coding (SDPC) and show that a single computational mechanism, pooling, allows the SDPC model to account for the emergence in V1 of complex cells with or without that of orientation maps, as observed in distinct species of mammals. In particular, we observed that pooling in the feature space is directly related to the orientation map formation while pooling in the retinotopic space is responsible for the emergence of a complex cells population. Introducing different forms of pooling in a predictive model of early visual processing as implemented in SDPC can therefore be viewed as a theoretical framework that explains the diversity of structural and functional phenomena observed in V1. |
| first_indexed | 2024-12-10T16:27:33Z |
| format | Article |
| id | doaj.art-eaf3823fe4b8401daf7fa762d590e129 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-10T16:27:33Z |
| publishDate | 2022-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-eaf3823fe4b8401daf7fa762d590e1292022-12-22T01:41:38ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-07-01187e101027010.1371/journal.pcbi.1010270Pooling strategies in V1 can account for the functional and structural diversity across species.Victor BoutinAngelo FranciosiniFrédéric ChavaneLaurent U PerrinetNeurons in the primary visual cortex are selective to orientation with various degrees of selectivity to the spatial phase, from high selectivity in simple cells to low selectivity in complex cells. Various computational models have suggested a possible link between the presence of phase invariant cells and the existence of orientation maps in higher mammals' V1. These models, however, do not explain the emergence of complex cells in animals that do not show orientation maps. In this study, we build a theoretical model based on a convolutional network called Sparse Deep Predictive Coding (SDPC) and show that a single computational mechanism, pooling, allows the SDPC model to account for the emergence in V1 of complex cells with or without that of orientation maps, as observed in distinct species of mammals. In particular, we observed that pooling in the feature space is directly related to the orientation map formation while pooling in the retinotopic space is responsible for the emergence of a complex cells population. Introducing different forms of pooling in a predictive model of early visual processing as implemented in SDPC can therefore be viewed as a theoretical framework that explains the diversity of structural and functional phenomena observed in V1.https://doi.org/10.1371/journal.pcbi.1010270 |
| spellingShingle | Victor Boutin Angelo Franciosini Frédéric Chavane Laurent U Perrinet Pooling strategies in V1 can account for the functional and structural diversity across species. PLoS Computational Biology |
| title | Pooling strategies in V1 can account for the functional and structural diversity across species. |
| title_full | Pooling strategies in V1 can account for the functional and structural diversity across species. |
| title_fullStr | Pooling strategies in V1 can account for the functional and structural diversity across species. |
| title_full_unstemmed | Pooling strategies in V1 can account for the functional and structural diversity across species. |
| title_short | Pooling strategies in V1 can account for the functional and structural diversity across species. |
| title_sort | pooling strategies in v1 can account for the functional and structural diversity across species |
| url | https://doi.org/10.1371/journal.pcbi.1010270 |
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