Fast Overcomplete Topographical Independent Component Analysis (FOTICA) and its Implementation using GPUs

Overcomplete and topographic representation of natural images is an important concept in computational neuroscience due to its similarity to the anatomy of visual cortex. In this paper, we propose a novel approach, which applies the fixed-point technique of the method called FastICA [1] to the ICA model with the properties of overcomplete and topographic representation, named Fast Overcomplete Topographic ICA (FOTICA). This method inherits the features of FastICA, such as faster time to convergence, simpler structure, and less parameters. The proposed FOTICA can easily be implemented in GPUs. In this paper, we also compare the performances with different system configurations. Through the comparison, we will show the performance of the proposed FOTICA and the power of implementing FOTICA using GPUs.