| Abstrakt: |
To understand the experience of seeing is difficult and is being pursued day and night, especially in psychology and neuroscience. However, there are hard areas to research, such as estimating the structure of receptive fields (RF) in the higher-order visual cortex with humans and animals as research targets. Deep neural networks (DNNs) are being reported that have similar properties to visual neurons and the possibility of using DNNs as alternative re- search targets to the biological brain has emerged. Therefore, in this paper, I discuss whether DNNs can be our re- search subject. In this research, I applied the reverse correlation method, which has revealed RF of visual neurons, to DNNs to estimate RF of units in well-trained VGG-16. As a result, the properties of the RF of VGG-16 units were similar to visual cortex neurons. The result suggested that DNNs may be a good alternative model for our research, but also suggested limitations of the method. To solve remaining problems, psychology research that develop the better methods and deep learning research that provides better alternative models must go hand in hand. [ABSTRACT FROM AUTHOR] |
