Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently
Xiao Liu · Xiaolong Zou · Zilong Ji · Gengshuo Tian · Yuanyuan Mi · Tiejun Huang · K. Y. Michael Wong · Si Wu

Wed Dec 11th 10:45 AM -- 12:45 PM @ East Exhibition Hall B + C #144

Experimental data has revealed that in addition to feedforward connections, there exist abundant feedback connections in a neural pathway. Although the importance of feedback in neural information processing has been widely recognized in the field, the detailed mechanism of how it works remains largely unknown. Here, we investigate the role of feedback in hierarchical information retrieval. Specifically, we consider a hierarchical network storing the hierarchical categorical information of objects, and information retrieval goes from rough to fine, aided by dynamical push-pull feedback from higher to lower layers. We elucidate that the push (positive) and pull (negative) feedbacks suppress the interferences due to neural correlations between different and the same categories, respectively, and their joint effect improves retrieval performance significantly. Our model agrees with the push-pull phenomenon observed in neural data and sheds light on our understanding of the role of feedback in neural information processing.

Author Information

Xiao Liu (Peking University)
Xiaolong Zou (Peking University)
Zilong Ji (Beijing Normal University)
Gengshuo Tian (Beijing Normal University)
Yuanyuan Mi (Weizmann Institute of Science)
Tiejun Huang (Peking University)
K. Y. Michael Wong (Department of Physics, Hong Kong University of Science and Technology)
Si Wu (Peking University)

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