Emergent componential coding of a handwritten image database by neural self-organisation

This paper demonstrates the unsupervised discovery of localised components in real image data, using images of much larger size than the small fragmen ts from which components have previously been extracted. The handwriting im ages used are also much more homogeneous than the random natural scenes use d in earlier demonstrations, containing components of a specific size-scale and structure. Because of this homogeneity, the components found are not w avelets covering a range of size scales: instead, they correspond to line- and curve-segments made by the pen. The objective function that is optimise d here encodes and reconstructs the data via a Markov process, and is also related to density modelling techniques. Several earlier theoretical and ex perimental results-can also be attributed to the form of neuron used here, including the extraction of words from continuous speech and the discovery of unknown transformation invariances via the controlled breaking of dynami cal symmetry.