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He came to the University of Washington for postdoctoral work in neuroscience and has been on the faculty ever since. He is currently Professor in the Departments of Physiology & Biophysics and DXARTS, Adjunct Prof. in Bioengineering and Core Staff in the Washington National Primate Research Center.
His overall research has concerned the neural control of limb movement in primates. This began with studies of monkeys’ ability to volitionally control the activity of brain cells. In this operant conditioning paradigm monkeys controlled a biofeedback meter arm with patterns of activity in motor cortex neurons. This work in 1969 first showed that neural activity could be used to drive an external device, and demonstrated the ability of the brain to volitionally control the activity of cortical neurons in variable patterns, phenomena that underlie much of the current work in brain-machine interfaces. He went on to investigate the functional organization of motor cortex cells controlling forearm muscles by documenting the correlational linkages of output cells with muscles in spike-triggered averages of EMG. He pioneered the recording of spinal interneurons in behaving monkeys and showed that spinal neurons share many properties of cortical cells, including preparatory activity prior to instructed movements. Other studies investigated the synaptic interactions between cortical neurons by using in vivo intracellular recordings and spike-triggered averages of membrane potentials. To elucidate neural computations in large-scale neural networks he developed dynamic recurrent network models that simulate the neural interactions generating behavior like target tracking and short-term memory. Most recently, his lab has developed an implantable recurrent brain-computer interface that can record activity of cortical cells during free behavior and convert this activity in real time to stimulation of cortex, spinal cord or muscles. This so-called “neurochip” creates a continuously operating artificial feedback loop that the brain can learn to incorporate into behavior. A second application of the neurochip is to produce changes in the strength of synaptic connections through activity-dependent stimulation. These two capacities of the recurrent brain-computer interface have promise for many basic research and clinical applications.
His overall research has concerned the neural control of limb movement in primates. This began with studies of monkeys’ ability to volitionally control the activity of brain cells. In this operant conditioning paradigm monkeys controlled a biofeedback meter arm with patterns of activity in motor cortex neurons. This work in 1969 first showed that neural activity could be used to drive an external device, and demonstrated the ability of the brain to volitionally control the activity of cortical neurons in variable patterns, phenomena that underlie much of the current work in brain-machine interfaces. He went on to investigate the functional organization of motor cortex cells controlling forearm muscles by documenting the correlational linkages of output cells with muscles in spike-triggered averages of EMG. He pioneered the recording of spinal interneurons in behaving monkeys and showed that spinal neurons share many properties of cortical cells, including preparatory activity prior to instructed movements. Other studies investigated the synaptic interactions between cortical neurons by using in vivo intracellular recordings and spike-triggered averages of membrane potentials. To elucidate neural computations in large-scale neural networks he developed dynamic recurrent network models that simulate the neural interactions generating behavior like target tracking and short-term memory. Most recently, his lab has developed an implantable recurrent brain-computer interface that can record activity of cortical cells during free behavior and convert this activity in real time to stimulation of cortex, spinal cord or muscles. This so-called “neurochip” creates a continuously operating artificial feedback loop that the brain can learn to incorporate into behavior. A second application of the neurochip is to produce changes in the strength of synaptic connections through activity-dependent stimulation. These two capacities of the recurrent brain-computer interface have promise for many basic research and clinical applications.
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Sergio Ruiz,Sangkyun Lee,Josue Luiz Dalboni da Rocha,Ander Ramos,Emanuele Pasqualotto, Ernesto Soares, Eliana García,Eberhard Fetz,Niels Birbaumer,Ranganatha Sitaram
crossref(2024)
bioRxiv (Cold Spring Harbor Laboratory) (2023)
FRONTIERS IN NEUROSCIENCE (2022): 1273627-1273627
ENeurono. 4 (2022): ENEURO.0336-22.2023
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