Co-evolutionary Learning in Liquid Architectures

Abstract

A large class of problems requires real-time processing of complex temporal inputs in real-time. These are difficult tasks for state-of-the-art techniques, since they require capturing complex structures and relationships in massive quantities of low precision, ambiguous noisy data. A recently-introduced Liquid-State-Machine (LSM) paradigm provides a computational framework for applying a model of cortical neural microcircuit as a core computational unit in classification and recognition tasks of real-time temporal data. We extend the computational power of this framework by closing the loop. This is accomplished by applying, in parallel to the supervised learning of the readouts, a biologically-realistic learning within the framework of the microcircuit. This approach is inspired by neurobiological findings from ex-vivo multi-cellular electrical recordings and injection of dopamine to the neural culture. We show that by closing the loop we obtain a much more effective performance with the new Co-Evolutionary Liquid Architecture. We illustrate the added value of the closed-loop approach to liquid architectures by executing a speech recognition task.