On the Extraction of High-Level Visual Features from Lateral Geniculate Nucleus Activity: A Rat Study

Abstract

The lateral geniculate nucleus (LGN) plays a vital role in visual information processing as an early stage in the visual system linking the retina with the visual cortex. Beyond its simple linear role, the LGN has been found to have a complex role in higher-order visual processing that is not fully understood. The aim of this study is to examine predicting high-level visual features from rat LGN firing activity. Extracellular neural activity of LGN neurons was recorded from 6 anesthetized rats in response to 4 × 8 checkerboard visual stimulation patterns using multi-electrode arrays. The first examined high-level feature is classifying the positions of the majority of white pixels in a visual pattern using the corresponding LGN activity. Three classes of patterns are identified in this task: majority in the top two rows, majority in the bottom two rows, or equal number of white pixels across the top and bottom halves of the pattern. The second examined high-level feature is estimating how far the white pixels are scattered in a visual stimulation pattern based on the corresponding LGN activity. Our results demonstrate that using LGN population activity achieves an ${F}_{1}$-score of 0.67 in the patterns classification and a root-mean-square error of 0.3 in the scatter estimation. Such performance outperforms that achieved using the visual stimulation patterns as inputs to the classification and scatter estimation methods. These results provide evidence that specific high-level visual features could be represented in the LGN; suggesting a critical role of the LGN in encoding visual information.