组会
- Topic: Explaining face representation in the primate brain using different computational models
- Speaker: Wen Bincheng
- Date: 2:00 P.M., Friday, Mar 18, 2022
- Place: The Fifth Meeting Room in Intelligent Building
- Abstract: Explaining face representation in the primate brain using different computational models
- Topic: A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence
- Speaker: Wen Bincheng
- Date: 2:00 P.M., Friday, Dec 24, 2021
- Place: The Fifth Meeting Room in Intelligent Building
- Abstract: A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence
- Topic: Beyond category-supervision: Computational support for domain-general pressures guiding human visual system representation
- Speaker: Wen Bincheng
- Date: 2:00 P.M., Thursday, Nov 11, 2021
- Place: The First Meeting Room in Automation Building
- Abstract: Beyond category-supervision: Computational support for domain-general pressures guiding human visual system representation
- Topic: Emergence of Visual Center-Periphery Spatial organization in Deep convolutional neural networks
- Speaker: Wen Bincheng
- Date: 9:00 A.M., Monday, Sep 13, 2021
- Place: The Fourth Meeting Room in Intelligent Building
- Abstract: Emergence of Visual Center-Periphery Spatial organization in Deep convolutional neural networks
- Topic: Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior
- Speaker: Wen Bincheng
- Date: 2:00 P.M., Friday, Jul 16, 2021
- Place: The Fifth Meeting Room in Intelligent Building
- Abstract: Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior
- Topic: Qualitative similarities and differences in visualobject representations between brains and deepnetworks
- Speaker: Wen Bincheng
- Date: 2:00 P.M., Friday, Apr 16, 2021
- Place: The Fourth Meeting Room in Intelligent Building
- Abstract: Qualitative similarities and differences in visualobject representations between brains and deepnetworks