Integrating Computational and Neural Findings in Visual Object Perception
Hans P. Op de Beeck
Integrating Computational and Neural Findings in Visual Object Perception - Frontiers Media SA 2016 - 1 electronic resource (137 p.)
Open Access
The articles in this Research Topic provide a state-of-the-art overview of the current progress in integrating computational and empirical research on visual object recognition. Developments in this exciting multidisciplinary field have recently gained momentum: High performance computing enabled breakthroughs in computer vision and computational neuroscience. In parallel, innovative machine learning applications have recently become available for datamining the large-scale, high resolution brain data acquired with (ultra-high field) fMRI and dense multi-unit recordings. Finally, new techniques to integrate such rich simulated and empirical datasets for direct model testing could aid the development of a comprehensive brain model. We hope that this Research Topic contributes to these encouraging advances and inspires future research avenues in computational and empirical neuroscience.
Creative Commons
English
978-2-88919-873-3 9782889198733
10.3389/978-2-88919-873-3 doi
object recognition fMRI multimodal data integration neural networks invariance computational neuroscience Computer Vision Feature representation Neurophysiology ventral visual pathway
Integrating Computational and Neural Findings in Visual Object Perception - Frontiers Media SA 2016 - 1 electronic resource (137 p.)
Open Access
The articles in this Research Topic provide a state-of-the-art overview of the current progress in integrating computational and empirical research on visual object recognition. Developments in this exciting multidisciplinary field have recently gained momentum: High performance computing enabled breakthroughs in computer vision and computational neuroscience. In parallel, innovative machine learning applications have recently become available for datamining the large-scale, high resolution brain data acquired with (ultra-high field) fMRI and dense multi-unit recordings. Finally, new techniques to integrate such rich simulated and empirical datasets for direct model testing could aid the development of a comprehensive brain model. We hope that this Research Topic contributes to these encouraging advances and inspires future research avenues in computational and empirical neuroscience.
Creative Commons
English
978-2-88919-873-3 9782889198733
10.3389/978-2-88919-873-3 doi
object recognition fMRI multimodal data integration neural networks invariance computational neuroscience Computer Vision Feature representation Neurophysiology ventral visual pathway