Unsupervised shape learning in a neuromorphic hierarchy

We present a neural-based learning system for object recognition in still gray-scale images. The system comprises several hierarchical levels of increasing complexity modeling the feed-forward path of the ventral stream in the visual cortex. It learns typical shape patterns of objects as these appear in images from experience alone without any prior labeling. Information about the exact origin of parts of the stimulus is systematically discarded, while the shape-related object identity information is preserved, resulting in strong compression of the original image data. To demonstrate it's capabilities, we train the system on publicly available image databases and use it's final output in classification tasks.