An Energy-Based Convolutional SOM Model with Self-adaptation Capabilities

摘要

We present a new self-organized neural model that we term ReST (Resilient Self-organizing Tissue). ReST can be run as a convolutional neural network (CNN), possesses a C~∞ energy function as well as a probabilistic interpretation of neural activities, which arises from the constraint of log-normal activity distribution over time that is enforced during learning. We discuss the advantages of a C~∞ energy function and present experiments demonstrating the self-organization and self-adaptation capabilities of ReST. In addition, we provide a performance benchmark for the publicly available TensorFlow-implementation.