Co-evolutionary Learning in Liquid Architectures

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 recentlyintroduced 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 exvivo 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 t...

• Type: Conference
• Year: 2005
• Where: IWANN
• Authors: Igal Raichelgauz, Karina Odinaev, Yehoshua Y. Zeevi
• Keywords: Complex Temporal Inputs, Cortical Neural Microcircuit, IWANN 2005, Recognition Task