Reconstructing feedback representations in the ventral visual pathway with a generative adversarial autoencoder

PLoS Comput Biol. 2021 Mar 24;17(3):e1008775. doi: 10.1371/journal.pcbi.1008775. eCollection 2021 Mar.


While vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understood. Here, we developed a generative autoencoder neural network model and adversarially trained it on a categorically diverse data set of images. We hypothesized that the feedback processes in the ventral visual pathway can be represented by reconstruction of the visual information performed by the generative model. We compared representational similarity of the activity patterns in the proposed model with temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) visual brain responses. The proposed generative model identified two segregated neural dynamics in the visual brain. A temporal hierarchy of processes transforming low level visual information into high level semantics in the feedforward sweep, and a temporally later dynamics of inverse processes reconstructing low level visual information from a high level latent representation in the feedback sweep. Our results append to previous studies on neural feedback processes by presenting a new insight into the algorithmic function and the information carried by the feedback processes in the ventral visual pathway.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms
  • Brain / diagnostic imaging
  • Brain / physiology
  • Computational Biology
  • Feedback, Physiological / physiology
  • Female
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Magnetic Resonance Imaging
  • Magnetoencephalography
  • Neural Networks, Computer*
  • Visual Cortex* / diagnostic imaging
  • Visual Cortex* / physiology
  • Visual Pathways* / diagnostic imaging
  • Visual Pathways* / physiology
  • Young Adult

Grant support

The study was supported by the Canada First Research Excellence Fund (CFREF) through Western’s BrainsCAN Initiative. The Computational modelling was conducted on Compute Canada resources. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.