Eberhard FetzDepartment of Physiology and Biophysics, University of Washington, Seattle, WA, USA F1000 Faculty Member (since 15 February 2009)
Professor, Department of Physiology and Biophysics, University of Washington
PhD, Massachusetts Institute of Technology, Physics, 1967
BS, Rensselaer Polytechnic Institute, Physics, 1961
American Association for the Advancement of Science
American Physiological Society
International Brain Research Organization
Neural Control of Movement
Society for Neuroscience
2004-2005, Fellow, Wissenschaftskolleg zu Berlin, Berlin
2002-2002, Visiting Professor, College de France, College de France, Paris, Neuroscience
1985-1986, NSF US-Japan Cooperative Science Program Award, National Science Foundation, Kyoto Prefectural University, Neuroscience
1977-1978, Faculty Scholar Award, Josiah Macy Foundation, Harvard and University of Goteborg, Neurophysiology
1970-1975, Teacher-Investigator Award, NINDS, University of Washington, Physiology and Neurological Surgery
We are investigating the neural mechanisms involved in programming and executing hand movements by recording neural activity in monkeys trained to manually track visual targets. We are particularly interested in studying 'premotoneuronal' cells in motor cortex and spinal cord that produce postspike effects on forelimb muscle activity. By knowing both the response patterns of these cells during movements and their output connections to target muscles we can make important causal inferences about their contribution to movements. The first recordings of spinal interneurons in behaving monkeys have revealed that spinal neurons share many properties of cortical neurons, including preparation for instructed movements.
We are currently developing an implantable 'brain-computer interface' to record activity of cortical neurons in monkeys and convert this activity to stimuli delivered at sites in motor cortex, spinal cord or muscles. An implanted array of microelectrodes records neural activity; and a computer chip discriminates action potentials and controls the stimulus parameters. We will study the behavioral adaptation of monkeys to the long-term presence of these artificial feedback loops.
In parallel with these physiological studies, we are also using neural network models to show how neural computation could be performed in large populations of cells. Dynamic network models are used to simulate the neural interactions generating behavior like target tracking. The networks transform multiple temporal input patterns (e.g. visual targets) to desired output patterns (e.g. discharge patterns of motor units recorded in monkeys). Such network modeling has provided important insights into the neural mechanisms that could mediate movement and short-term memory.
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