Eve Marder
Brandeis University
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Primary Section: 24, Cellular and Molecular Neuroscience Secondary Section: 28, Systems Neuroscience Membership Type:
Member
(elected 2007)
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Biosketch
For my entire scientific career I have been dedicated to the principle that outstanding science must go hand-in-hand with excellent education. To that end, I teach undergraduates and graduate students in the classroom, and my laboratory combines undergraduates, graduate students and postdocs who work together. I take pride in the accomplishments of my trainees of all levels, and hope to help each and every trainee find their way to success, in whatever role or venue best suits him or her. I believe that the skills and discipline that scientific inquiry engender will serve my trainees well, whether they continue in academic science, move into medicine, industry, politics, or other careers. I also believe that the pursuit of science in individual labs must be coupled with contributions to our joint community, because science as we now practice it is essentially a communal enterprise. Consequently, I have always combined editorial service, grant evaluation duties, and other activities with my own work.
Research Interests
For many years I have been using the small circuits of the crustacean stomatogastric nervous system as a platform with which to study how the dynamics of neuronal circuits depend on the interaction of their underlying synaptic connections and the intrinsic properties of their constitutent neurons. My early work was instrumental in demonstrating that neuronal circuits are not "hard-wired" but can be reconfigured by neuromodulatory neurons and substances to produce a variety of outputs. Together with Larry Abbott, my laboratory pioneered the "dynamic clamp". Our work today combines experimental and theoretical approaches to understand the homeostatic processes that maintain network stability while allowing for the flexibility needed for behavior. Additionally, we study the extent to which similar network performance can arise from different sets of underlying network parameters.
