Clustering with minicolumnar receptive field self-organization

We study clustering, i.e., unsupervised data classification, by a model of the cortical macrocolumn. Continuous valued input vectors are encoded using a population place code. The macrocolumn model self-organizes its minicolumnar receptive fields (RFs) such that the input is hierarchically subdivided into increasingly finer classes. If input superpositions are used for training, the system is able to find an appropriate classification of the input and a suitable representation of input superpositions. Together with fast reaction times the model satisfies major requirements of biological information processing and distinguishes itself from other suggested models of continuous value processing in biological neural networks.