Applied Brain Research

ABR's new Legendre Memory Unit (LMU) is a neuromorphic algorithm for continuous-time memory that can learn temporal dependencies over millions of time-steps or more. The Legendre Memory Unit (LMU) is a new RNN architecture that enables networks of artificial neurons to classify and predict temporal patterns far more efficiently than LSTMs. The LMU is mathematically derived to implement the continuous-time dynamical system that optimally maintains a scale-invariant representation of time. The ABR LMU obtains the best-known results on permuted sequential MNIST, a difficult RNN benchmark, and has been shown to scale to input sequences spanning hundreds of millions of time-steps. The resulting patterns in spiking activity have also been linked to neural "time cells" observed in the striatum and medial prefrontal cortex in mammalian brains. Unlike the LSTM, the LMU can be implemented using spiking neurons, thus demonstrating an algorithmic advance that is anticipated to provide leaps in efficiency for solutions to dynamical time-series problems using low-power neuromorphic devices.