Literature DB >> 29870929

Efficient coding matters in the organization of the early visual system.

Qingqun Kong1, Jiuqi Han2, Yi Zeng3, Bo Xu4.   

Abstract

Individual areas in the brain are organized into a hierarchical network as a result of evolution. Previous work indicated that the receptive fields (RFs) of individual areas have been evolved to favor metabolically efficient neural codes. In this paper, we propose that not only the RFs of individual areas, but also the organization of adjacent neurons and the hierarchical structure composed of these areas have been evolved to support efficient coding. To verify this hypothesis, we introduce a feed-forward three-layer network to simulate the early stages of human visual system. We emphasize that the network is not a purely feed-forward one since it also includes intra-layer connections, which are essential but usually ignored in the literature. Simulation results strongly reveal that (1) the obtained RFs of the simulated retinal ganglion cells (RGCs) or neurons in the lateral geniculate nucleus (LGN) and V1 simple neurons are consistent to the neurophysiological data; (2) the responses of closer RGCs are more correlated, and V1 simple neurons with similar orientations prefer to cluster together; (3) the hierarchical organization of the early visual system is beneficial for saving energy, which accords with the requirement of metabolically efficient neural coding in the process of human brain evolution.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Brain-inspired neural networks; Early visual stages; Efficient coding; Hierarchical structure

Mesh:

Year:  2018        PMID: 29870929     DOI: 10.1016/j.neunet.2018.04.019

Source DB:  PubMed          Journal:  Neural Netw        ISSN: 0893-6080


  1 in total

1.  Directional Preference in Avian Midbrain Saliency Computing Nucleus Reflects a Well-Designed Receptive Field Structure.

Authors:  Jiangtao Wang; Longlong Qian; Songwei Wang; Li Shi; Zhizhong Wang
Journal:  Animals (Basel)       Date:  2022-04-28       Impact factor: 3.231

  1 in total

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