Zou, X;
Ji, Z;
Zhang, T;
Huang, T;
Wu, S;
(2023)
Visual information processing through the interplay between fine and coarse signal pathways.
Neural Networks
, 166
pp. 692-703.
10.1016/j.neunet.2023.07.048.
Preview |
PDF
Ji_Visual information processing through the interplay between fine and coarse signal pathways_VoR.pdf - Published Version Download (1MB) | Preview |
Abstract
Object recognition is often viewed as a feedforward, bottom-up process in machine learning, but in real neural systems, object recognition is a complicated process which involves the interplay between two signal pathways. One is the parvocellular pathway (P-pathway), which is slow and extracts fine features of objects; the other is the magnocellular pathway (M-pathway), which is fast and extracts coarse features of objects. It has been suggested that the interplay between the two pathways endows the neural system with the capacity of processing visual information rapidly, adaptively, and robustly. However, the underlying computational mechanism remains largely unknown. In this study, we build a two-pathway model to elucidate the computational properties associated with the interactions between two visual pathways. Specifically, we model two visual pathways using two convolution neural networks: one mimics the P-pathway, referred to as FineNet, which is deep, has small-size kernels, and receives detailed visual inputs; the other mimics the M-pathway, referred to as CoarseNet, which is shallow, has large-size kernels, and receives blurred visual inputs. We show that CoarseNet can learn from FineNet through imitation to improve its performance, FineNet can benefit from the feedback of CoarseNet to improve its robustness to noise; and the two pathways interact with each other to achieve rough-to-fine information processing. Using visual backward masking as an example, we further demonstrate that our model can explain visual cognitive behaviors that involve the interplay between two pathways. We hope that this study gives us insight into understanding the interaction principles between two visual pathways.
| Type: | Article |
|---|---|
| Title: | Visual information processing through the interplay between fine and coarse signal pathways |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.neunet.2023.07.048 |
| Publisher version: | https://doi.org/10.1016/j.neunet.2023.07.048 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Backward masking, Convolution neural network, Imitation learning, Two-pathway model, Visual information processing |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Experimental Epilepsy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10176188 |
Archive Staff Only
 |
View Item |
