Producing pattern examples from "mental" images

The WiSARD (Wilkie, Stonham and Aleksander's Recognition Device) weightless neural network model has its functionality based on the collective response of RAM-based neurons. WiSARD's learning phase consists on writing at the RAM neurons' positions addressed (typically through a pseudo-random mapping) by binary training patterns. By counting the frequency of writing accesses at RAM neuron positions during the learning phase, it is possible to associate the most accessed addresses with the corresponding input field contents that defined them. The idea of associating this process with the formation of ''mental'' images is explored in the DRASiW model, a WiSARD extension provided with the ability of producing pattern examples, or prototypes, derived from learnt categories. This work demonstrates the equivalence of two ways of generating such prototypes: (i) via frequency counting and filtering and (ii) via formulating fuzzy rules. Moreover, it is shown, through the exploration of the MNIST database of handwritten digits as benchmark, how the process of mental images formation can improve WiSARD's classification skills.