Development of a Bidirectional Associative Memory for Supervised, Unsupervised and Reinforcement Learning

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Development of a Bidirectional Associative Memory for Supervised, Unsupervised and Reinforcement Learning. Although it is something most of us do every day without effort, memorizing is in fact an incredibly complex task. For instance, the simple act of storing and retrieving an object is something we have not yet managed to program a machine to do with anything approaching human efficiency and robustness. My research aims to better understand how human cognitive system accomplishes the complex task of create (and enhance) representation from patterns as well as recognize, identify, categorize and classify them. To understand how the human cognitive system works, we need to develop formal models. Nowadays, no formal models can take into account this variety of behavior without scarifying simplicity and self-consistency. In this research, we will use artificial neural networks that are parallel and where information is distributed among the units. Since the brain uses similar computing units and mechanisms for different tasks, this approach uses a bidirectional associative memory (BAM) model as building blocks for a general cognitive architecture. More precisely, this research program focuses on developing a general BAM that can encompass supervised, unsupervised and reinforcement learning. Focus is given on interesting nonlinear dynamics system perspective where time and change are the key variables. Within that perspective memory are represented as invariant or aperiodic states. Moreover, we want to build a better grounding into neuroscience by extending the BAM to handle spiking representation and implement the model into virtual and physical robots. Therefore, it is hoped that by developing such BAM we will have a better understanding on how the brain and mind work, and, how this understanding can be translated into artificial general intelligence.