Biologically Inspired Augmented Memory Recall Model for Pattern Recognition

The concept of modeling a machine which can adapt to the dynamic changes in environment has fascinated the field of Artificial Intelligence. Machine Learning has made inroads in every possible domain. New techniques are developed which can mimic human like responses and thoughts. Cognitive computing has developed renewed interest in the community with advent of Artificial Neural Nets (ANN). In this paper, we present a biological inspired approach to building a augmented memory recall model which can learn usage access patterns and reconstruct from them when presented with noisy or broken concepts. We use Hopfield Networks in a distributed parallel architecture like Hadoop. We also present a mechanism for augmenting the memory capacity of Hopfield Nets. Our model is tested on a real world dataset by parallelizing the learning process thereby increasing the computing power to recognize patterns.