Extraction of Independent Causes Using Lateral Connections
This paper is an exploration of a straightforward but powerful unsupervised artificial neural network technique for the efficient coding of artificial and real data. Using an energy function based on the Rectified Gaussian distribution, we derive a neural architecture based on an unsupervised negative feedback network with fixed lateral connections. We show that, not only can this network identify local correlated structure in visual data but through the use of appropriate lateral connections, we can obtain a grouping of similar causes on the output response of the network. We show that the network may be used to form local spatiotemporal filters in response to real images contained in video. The shape of these filters reflects the nature of the video sequences.