Qihong Lu

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1. A recurrent neural network for human object recognition

The speed of ultra-rapid categorization (Wu, et al., 2015) has been considered as evidence for a feed-forward view of visual recognition (Serre, Oliva & Poggio, 2007). Here, we built a recurrent neural network that captures the behavioral and neural temporal dynamics of visual recognition processes, including the rapid recognition data and several other empirical patterns. These results provide evidence that object recognition is supported by interactive processes in the brain.
- [
CogSci 2016 Poster,
CogSci 2016 Abstract,
Code
]

We investigated the localization of the neural representations of faces, places, and objects. We developed a novel sparse multi-voxel pattern analysis (MVPA) method, which identifies a subset of brain regions (voxels) that predict the kind of pictures (e.g. face vs. non-face) presented to the participants, given their fMRI data. Besides some classic ROIs (e.g. Kanwisher, McDermott, and Chun, 1997), we also found a bunch of "extra" brain regions that are distributed, signal-carrying and idiosyncratic across subjects.
- [
CNS 2015 Poster
,
Code
]