A Neural Field Model for Supervised and Unsupervised Learning of the MNIST Dataset
Michael Brady · 2019
A biological model of cortical learning based on neural fields is elaborated and analyzed. In an effort to establish the method as a viable option in today's machine learning world, the method is evaluated based on the MNIST dataset. The approach relates to established work in dynamic systems and neural field models, with an innovation that prevents runaway feedback between neural fields. The method is advanced through four studies. The first three studies illustrate the mechanics of the method by way of supervised learning. Results are compared to analogous results achieved through other methods. The final study builds on the first three studies to illustrate and evaluate how unsupervised learning is accomplished. Concluding discussion considers advantages of the approach over other approaches, and how large neural field networks may be constructed and applied to temporal pattern learning in domains such as robotics.