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Exploring Brain Effective Connectivity in Visual Perception Using a Hierarchical Correlation Network

  • Siyu Yu
  • Nanning ZhengEmail author
  • Hao Wu
  • Ming Du
  • Badong Chen
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 559)

Abstract

Brain-inspired computing is a research hotspot in artificial intelligence (AI). One of the key problems in this field is how to find the bridge between brain connectivity and data correlation in a connection-to-cognition model. Functional magnetic resonance imaging (fMRI) signals provide rich information about brain activities. Existing modeling approaches with fMRI focus on the strength information, but neglect structural information. In a previous work, we proposed a monolayer correlation network (CorrNet) to model the structural connectivity. In this paper, we extend the monolayer CorrNet to a hierarchical correlation network (HcorrNet) by analysing visual stimuli of natural images and fMRI signals in the entire visual cortex, that is, V1, V2 V3, V4, fusiform face area (FFA), the lateral occipital complex (LOC) and parahippocampal place area (PPA). Through the HcorrNet, the efficient connectivity of the brain can be inferred layer by layer. Then, the stimulus-sensitive activity mode of voxels can be extracted, and the forward encoding process of visual perception can be modeled. Both of them can guide the decoding process of fMRI signals, including classification and image reconstruction. In the experiments, we improved a dynamic evolving spike neuron network (SNN) as the classifier, and used Generative Adversarial Networks (GANs) to reconstruct image.

Keywords

Brain-inspired computing Visual perception Functional magnetic resonance imaging (fMRI) Hierarchical correlation network (HcorrNet) Connection 

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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Siyu Yu
    • 1
    • 2
  • Nanning Zheng
    • 1
    • 2
    Email author
  • Hao Wu
    • 1
    • 2
  • Ming Du
    • 1
    • 2
  • Badong Chen
    • 1
    • 2
  1. 1.Institute of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityXi’anPeople’s Republic of China
  2. 2.National Engineering Laboratory for Visual Information Processing and ApplicationsXi’an Jiaotong UniversityXi’anPeople’s Republic of China

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