Towards Bridging the Gap Between Pattern Recognition and Symbolic Representation Within Neural Networks

Underlying symbolic representations are opaque within neural networks that perform pattern recognition. Neural network weights are sub-symbolic, they commonly do not have a direct symbolic correlates. This work shows that by implementing network dynamics differently, during the testing phase instead of the training phase, pattern recognition can be performed using symbolically relevant weights. This advancement is an important step towards the merging of neural-symbolic representation, memory, and reasoning with pattern recognition.