Stephen M. Gordon

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OF TALK Engineers have long used control systems utilizing models and feedback loops to control real-world systems. Limitations of model-based control led to a generation of intelligent control techniques such as adaptive and fuzzy control. Human brain, on the other hand, is known to process a variety of inputs in parallel, ignore distractions to focus on(More)
Decades of heavy investment in laboratory-based brain imaging and neuroscience have led to foundational insights into how humans sense, perceive, and interact with the external world. However, it is argued that fundamental differences between laboratory-based and naturalistic human behavior may exist. Thus, it remains unclear how well the current knowledge(More)
Detecting significant periods of phase synchronization in EEG recordings is a non-trivial task that is made especially difficult when considering the effects of volume conduction and common sources. In addition, EEG signals are often confounded by non-neural signals, such as artifacts arising from muscle activity or external electrical devices. A variety of(More)
– This paper discusses an application of cognitive control for robot task execution. The idea is currently being implemented in a humanoid robot ISAC using the Central Executive Agent and the Working Memory System. Using cognitive control, the robot should be able to learn how to execute task using past experience and emotion. This paper also discusses a(More)
– As a robot learns behaviors and task execution, several systems must be in place to allow the robot to store what has been learned as well as to recall learned information. We believe having a long-term memory is essential for our robot to be able to learn tasks and behaviors over time. We also believe that it is important for the robot to have some means(More)