Journal
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
Volume 14, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fncom.2020.00029
Keywords
attention; artificial neural networks; machine learning; vision; memory; awareness
Funding
- Marie Sklodowska-Curie Individual Fellowship [844003]
- Sainsbury Wellcome Centre/Gatsby Computational Unit Fellowship
- Marie Curie Actions (MSCA) [844003] Funding Source: Marie Curie Actions (MSCA)
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Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This review starts by providing an overview of how attention is conceptualized in the neuroscience and psychology literature. It then covers several use cases of attention in machine learning, indicating their biological counterparts where they exist. Finally, the ways in which artificial attention can be further inspired by biology for the production of complex and integrative systems is explored.
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