A Self-Organization Model of Feature Columns and Face Responsive Neurons in the Temporal Cortex

We investigate a self-organizing network model to account for the computational property of the inferotemporal cortex. The network can learn sparse codes for given data with organizing their topographic mapping. Simulation experiments are performed using real face images composed of different individuals at different viewing directions, and the results show that the network evolves the information representation which is consistent with some physiological findings. By analyzing the characteristics of the neuron activities, it is also demonstrated that the present model self-organizes the efficient representation for coding both of the global structure and the finer information of the face images.