Making neural nets simple enough to succeed at universal relational generalization

Traditional brain-style (connectionist) approaches basically hit a wall when it comes to relational cognition. As an alternative to the well-known approaches of structured connectionism and deep learning, I present an engine for relational pattern recognition based on minimalist reinterpretations of first principles of connectionism. Results of computational experiments will be discussed on problems testing relational learning and universal generalization.