Abstract
TurSOM [1], short for Turing Self-Organizing Map, introduces new concepts, responsibilities and mechanisms to the traditional SOM algorithm. It draws its inspiration from Turing Unorganized Machines, competitive learning techniques, and SOM algorithms. Turing's unorganized machines (TUM) were one of the first computational concepts of modeling the cortex. Turing also described these machines as having self-organizing behaviors. The primary difference between Turing's self-organization description, and more traditional models we are familiar with (Grossberg, Kohonen), are that connections, rather than neurons, self-organize.
TurSOM adheres to unsupervised, competitive learning techniques, wherein all neurons, and all connections between them are self-organizing and competing. As such, it presents a novel self-organizing neural network algorithm that eliminates the need for post-processing methods for cluster identification.