Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.  extend the computational por 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.  show that by closing the loop  obtain a much more effective performance with the new Co-Evolutionary Liquid Architecture.  illustrate the added value of the closed-loop approach to liquid architectures by executing a speech recognition task.
['Igal Raichelgauz', 'Karina Odinaev', 'Yehoshua Y. Zeevi']
I am writing a research paper on a idea Co-evolutionary learning in liquid architectures
[{"from": "human", "value": "I am writing a research paper on a idea Co-evolutionary learning in liquid architectures"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.  extend the computational por 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.  show that by closing the loop  obtain a much more effective performance with the new Co-Evolutionary Liquid Architecture.  illustrate the added value of the closed-loop approach to liquid architectures by executing a speech recognition task."}]