Name
Title | ||
|---|---|---|
Editorial: Integrating Computational and Neural Findings in Visual Object Perception. |
Abstract | ||
|---|---|---|
Recognizing objects despite infinite variations in their appearance is a highly challenging computational task the visual system performs in a remarkably fast, accurate, and robust fashion. The complexity of the underlying mechanisms is reflected in the large proportion of cortical real-estate dedicated to visual processing, as well as in the difficulties encountered when trying to build models whose performance matches human proficiency.The articles in this Research Topic provide an overview of recent advances in our understanding of the neural mechanisms underlying visual object perception, focusing on integrative approaches which encompass both computational and empirical work. Given the vast expanse of topics covered in the discipline of computational visual neuroscience, it is impossible to provide a comprehensive overview of the fieldu0027s status-quo. Instead, the presented papers highlight interesting extensions to existing models and novel insights into computational principles and their neural underpinnings. Contributions could be coarsely subdivided into three different sections: Two papers focused on implementing biologically-valid learning rules and heuristics in well-established neural models of the visual pathway (i.e., “VisNet” and “HMAX”) to improve flexible object recognition. Three other studies investigated the role of sparseness, selectivity, and correlation in optimizing neural coding of object features. Finally, another set of contributions focused on integrating computational vision models and human brain responses to gain more insights in the computational mechanisms underlying neural object representations. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3389/fncom.2016.00036 | FRONTIERS IN COMPUTATIONAL NEUROSCIENCE |
Keywords | Field | DocType |
object recognition,computer vision,fMRI,feature representation,ventral visual pathway,invariance | Neuroscience,Visual processing,Computational vision,Computer science,Neural coding,Heuristics,Artificial intelligence,Perception,Machine learning,Cognitive neuroscience of visual object recognition | Journal |
Volume | ISSN | Citations |
10 | 1662-5188 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Judith Peters | 1 | 75 | 7.19 |
| Hans P. Op de Beeck | 2 | 142 | 16.80 |
| Rainer Goebel | 3 | 670 | 56.00 |