How representations coded by neurons in deep neural networks differ from neural data

I show how using an approach from signal detection theory can be applied to study high-level representations in a deep neural network to identify sensitive detectors. The results suggest that category-selective filters can be observed from the first layers of deep neural networks. I also show that a tuning curve of a category-selective filter differs from what is usually seen in a neural data. Representations of objects in weights of convolutional layers do not necessarily reflect the perceptual similarity of images. Instead, they are strongly dependent on a choice of a training set and cost function. This properties of neural networks may lead to the behavior when a model detect objects in their absence and fails to recognize obvious cases from a human-observer point of view.