Eberhard Fetz

BIOGRAPHY

ACADEMIC POSITION:
Professor, Department of Physiology and Biophysics, University of Washington

EDUCATION:
• PhD, Massachusetts Institute of Technology, Physics, 1967
• BS, Rensselaer Polytechnic Institute, Physics, 1961

MEMBERSHIP:
• American Association for the Advancement of Science
• American Physiological Society
• International Brain Research Organization
• Neural Control of Movement
• Sigma Xi
• Society for Neuroscience

AWARDS:
• 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

RESEARCH INTERESTS:
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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