A bio-inspired hardware implementation of an analog spike-based hippocampus memory model

The need for processing at the edge the increasing amount of data that is being produced by multitudes of sensors has led to the demand for mode power-efficient computational systems, by exploring alternative computing paradigms and technologies. Neuromorphic engineering is a promising approach that can address this need by developing electronic systems that faithfully emulate the computational properties of animal brains. In particular, the hippocampus stands out as one of the most relevant brain region for implementing auto associative memories capable of learning large amounts of information quickly and recalling it efficiently. In this work, we present a computational spike-based memory model inspired by the hippocampus that takes advantage of the features of analog electronic circuits: energy efficiency, compactness, and real-time operation. This model can learn memories, recall them from a partial fragment and forget. It has been implemented as a Spiking Neural Networks directly on a mixed-signal neuromorphic chip. We describe the details of the hardware implementation and demonstrate its operation via a series of benchmark experiments, showing how this research prototype paves the way for the development of future robust and low-power mixed-signal neuromorphic processing systems. • First fully functional neuromorphic analog memory model bioinspired by hippocampus. • Model implemented with SNNs on DYNAP-SE2 analog/digital hardware platform. • The model is able to learn, recall from fragments, and forget information in real time. • Analog vs digital memory model comparison, defining benefits and drawbacks.