A RESERVOIR COMPUTING MODEL OF EPISODIC MEMORY

We present a novel neural episodic memory architecture that utilizes reservoir computing to extract and recall information gleaned over time from a multilayer perceptron that receives sensory input. Reservoir computing models project input data into a high-dimensional dynamical space and also serve as a fading memory that holds on to past inputs thereby enabling the direct association of the current input with the past. The architecture presented utilizes these capabilities via an abstract feedback mechanism and in doing so creates attractor-like states within the reservoir that are associated with each discrete memory and associates these states and therefore memories over time into episodes. In addition, the feedback mechanism provides stabilization to an otherwise chaotic complex dynamical system.