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Neural Mechanisms of Cortical Motion Computation Based on a Neuromorphic Sensory System
The visual cortex analyzes motion information along hierarchically arranged visual areas that interact through bidirectional interconnections. This work suggests a bio-inspired visual model focusing on the interactions of the cortical areas in which a new mechanism of feedforward and feedback proces...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4640561/ https://www.ncbi.nlm.nih.gov/pubmed/26554589 http://dx.doi.org/10.1371/journal.pone.0142488 |
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author | Abdul-Kreem, Luma Issa Neumann, Heiko |
author_facet | Abdul-Kreem, Luma Issa Neumann, Heiko |
author_sort | Abdul-Kreem, Luma Issa |
collection | PubMed |
description | The visual cortex analyzes motion information along hierarchically arranged visual areas that interact through bidirectional interconnections. This work suggests a bio-inspired visual model focusing on the interactions of the cortical areas in which a new mechanism of feedforward and feedback processing are introduced. The model uses a neuromorphic vision sensor (silicon retina) that simulates the spike-generation functionality of the biological retina. Our model takes into account two main model visual areas, namely V1 and MT, with different feature selectivities. The initial motion is estimated in model area V1 using spatiotemporal filters to locally detect the direction of motion. Here, we adapt the filtering scheme originally suggested by Adelson and Bergen to make it consistent with the spike representation of the DVS. The responses of area V1 are weighted and pooled by area MT cells which are selective to different velocities, i.e. direction and speed. Such feature selectivity is here derived from compositions of activities in the spatio-temporal domain and integrating over larger space-time regions (receptive fields). In order to account for the bidirectional coupling of cortical areas we match properties of the feature selectivity in both areas for feedback processing. For such linkage we integrate the responses over different speeds along a particular preferred direction. Normalization of activities is carried out over the spatial as well as the feature domains to balance the activities of individual neurons in model areas V1 and MT. Our model was tested using different stimuli that moved in different directions. The results reveal that the error margin between the estimated motion and synthetic ground truth is decreased in area MT comparing with the initial estimation of area V1. In addition, the modulated V1 cell activations shows an enhancement of the initial motion estimation that is steered by feedback signals from MT cells. |
format | Online Article Text |
id | pubmed-4640561 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-46405612015-11-13 Neural Mechanisms of Cortical Motion Computation Based on a Neuromorphic Sensory System Abdul-Kreem, Luma Issa Neumann, Heiko PLoS One Research Article The visual cortex analyzes motion information along hierarchically arranged visual areas that interact through bidirectional interconnections. This work suggests a bio-inspired visual model focusing on the interactions of the cortical areas in which a new mechanism of feedforward and feedback processing are introduced. The model uses a neuromorphic vision sensor (silicon retina) that simulates the spike-generation functionality of the biological retina. Our model takes into account two main model visual areas, namely V1 and MT, with different feature selectivities. The initial motion is estimated in model area V1 using spatiotemporal filters to locally detect the direction of motion. Here, we adapt the filtering scheme originally suggested by Adelson and Bergen to make it consistent with the spike representation of the DVS. The responses of area V1 are weighted and pooled by area MT cells which are selective to different velocities, i.e. direction and speed. Such feature selectivity is here derived from compositions of activities in the spatio-temporal domain and integrating over larger space-time regions (receptive fields). In order to account for the bidirectional coupling of cortical areas we match properties of the feature selectivity in both areas for feedback processing. For such linkage we integrate the responses over different speeds along a particular preferred direction. Normalization of activities is carried out over the spatial as well as the feature domains to balance the activities of individual neurons in model areas V1 and MT. Our model was tested using different stimuli that moved in different directions. The results reveal that the error margin between the estimated motion and synthetic ground truth is decreased in area MT comparing with the initial estimation of area V1. In addition, the modulated V1 cell activations shows an enhancement of the initial motion estimation that is steered by feedback signals from MT cells. Public Library of Science 2015-11-10 /pmc/articles/PMC4640561/ /pubmed/26554589 http://dx.doi.org/10.1371/journal.pone.0142488 Text en © 2015 Abdul-Kreem, Neumann http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Abdul-Kreem, Luma Issa Neumann, Heiko Neural Mechanisms of Cortical Motion Computation Based on a Neuromorphic Sensory System |
title | Neural Mechanisms of Cortical Motion Computation Based on a Neuromorphic Sensory System |
title_full | Neural Mechanisms of Cortical Motion Computation Based on a Neuromorphic Sensory System |
title_fullStr | Neural Mechanisms of Cortical Motion Computation Based on a Neuromorphic Sensory System |
title_full_unstemmed | Neural Mechanisms of Cortical Motion Computation Based on a Neuromorphic Sensory System |
title_short | Neural Mechanisms of Cortical Motion Computation Based on a Neuromorphic Sensory System |
title_sort | neural mechanisms of cortical motion computation based on a neuromorphic sensory system |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4640561/ https://www.ncbi.nlm.nih.gov/pubmed/26554589 http://dx.doi.org/10.1371/journal.pone.0142488 |
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