  Visual information is processed in hierarchically organized parallel pathways
in the primate brain. In lower cortical areas, color information and shape
information are processed in a parallel manner, while in higher cortical areas,
various types of visual information, such as color, face, animate/inanimate,
are processed in a parallel manner. In the present study, the possibility of
spontaneous segregation of visual information in parallel streams was examined
by constructing a convolutional neural network with parallel architecture in
all of the convolutional layers. The results revealed that color information
was segregated from shape information in most model instances. Deletion of the
color-related stream decreased recognition accuracy in the inanimate category,
whereas deletion of the shape-related stream decreased recognition accuracy in
the animate category. The results suggest that properties of filters and
functions of a stream are spontaneously segregated in parallel streams of
neural networks.
