Publications

View‐tuned and view‐invariant face encoding in IT cortex is explained by selected natural image fragments.

Published in Scientific Report, 2021

Humans recognize individual faces regardless of variation in the facial view. The view-tuned face neurons in the inferior temporal (IT) cortex are regarded as the neural substrate for view-invariant face recognition. This study approximated visual features encoded by these neurons as combinations of local orientations and colors, originated from natural image fragments. The resultant features reproduced the preference of these neurons to particular facial views. We also found that faces of one identity were separable from the faces of other identities in a space where each axis represented one of these features. These results suggested that view-invariant face representation was established by combining view sensitive visual features. The face representation with these features suggested that, with respect to view-invariant face representation, the seemingly complex and deeply layered ventral visual pathway can be approximated via a shallow network, comprised of layers of low-level processing for local orientations and colors (V1/V2-level) and the layers which detect particular sets of low-level elements derived from natural image fragments (IT-level).

Recommended citation: Nam, Y., Sato, T., Uchida, G. et al. View-tuned and view-invariant face encoding in IT cortex is explained by selected natural image fragments. Sci Rep 11, 7827 (2021). https://doi.org/10.1038/s41598-021-86842-7 https://www.nature.com/articles/s41598-021-86842-7

Visualization of information encoded by neurons in the higher-level areas of the visual system

Published in Journal of Optical Technology c/c of Opticheskii Zhurnal, 2018

This paper introduces an application of artificial neural networks for visualization of functions of neurons in the higher visual areas of the brain. First, a model that enables the prediction of an evoked neural response was implemented. The model has a correlation coefficient of up to 0.82 for certain cortical columns. Then, an approach to explaining representations encoded by neurons was proposed. The approach is based on generating images maximizing an activation in the model. A comparison of the visualization results with the experimental data suggests that the approach can be used to study the properties of the higher-level areas of the visual cortex.

Recommended citation: Malakhova, K. (2018). "Visualization of information encoded by neurons in the higher-level areas of the visual system." Journal of Optical Technology. 85. 494. 10.1364/JOT.85.000494.