Analysis based on neural representation of natural object surfaces to elucidate the mechanisms of a trained AlexNet model.

Analysis and understanding of trained deep neural networks (DNNs) can deepen our understanding of the visual mechanisms involved in primate visual perception. However, due to the limited availability of neural activity data recorded from various cortical areas, the correspondence between the characteristics of artificial and biological neural responses for visually recognizing objects remains unclear at the layer level of DNNs. In the current study, we investigated the relationships between the artificial representations in each layer of a trained AlexNet model (based on a DNN) for object classification and the neural representations in various levels of visual cortices such as the primary visual (V1), intermediate visual (V4), and inferior temporal cortices.

Correspondence between Monkey Visual Cortices and Layers of a Saliency Map Model Based on a Deep Convolutional Neural Network for Representations of Natural Images.

Attentional selection is a function that allocates the brain's computational resources to the most important part of a visual scene at a specific moment. Saliency map models have been proposed as computational models to predict attentional selection within a spatial location. Recent saliency map models based on deep convolutional neural networks (DCNNs) exhibit the highest performance for predicting the location of attentional selection and human gaze, which reflect overt attention.

Automatic analysis of composite physical signals using non-negative factorization and information criterion.

In time-resolved spectroscopy, composite signal sequences representing energy transfer in fluorescence materials are measured, and the physical characteristics of the materials are analyzed. Each signal sequence is represented by a sum of non-negative signal components, which are expressed by model functions. For analyzing the physical characteristics of a measured signal sequence, the parameters of the model functions are estimated.