Eve Marder is a University Professor and the Victor and Gwendolyn Beinfield Professor of Neuroscience at
Brandeis University. At Brandeis, Marder is also a member of the Volen National Center for Complex Systems. Dr. Marder is known for her pioneering work on small neuronal networks which her team has interrogated via a combination of complementary experimental and theoretical techniques.
Marder is particularly well known in the community for her work on neural circuits in the crustacean
stomatogastric nervous system (STNS), a small network of 30 neurons. She discovered that circuits are not “hard-wired” to produce a single output or behavior, but can be reconfigured by
neuromodulators to produce many outputs and behaviors while still maintaining the integrity of the circuit. Her work has revolutionized the way scientists approach the studies of
neural circuits with respect to the study of structural and functional behavior. The general principles that have resulted from her work are thought to be generally applicable to other neural networks, including those in humans.
Marder was born in Manhattan and raised on the east coast. Although she loved biology from an early age, Marder has shared that she held very diverse academic interests prior to starting her undergraduate degree and in fact entered
Brandeis University as an undergraduate in 1965 with a plan to study politics and become a lawyer.[1] She would instead find herself re-captivated by the world of biology and switched majors to Biology after her freshman year. Marder has shared that a pivotal turning point in her scientific self-development was writing a paper on
schizophrenia during an abnormal psychology class during her junior year. Her subsequent library studies on inhibition in neural signaling solidified her career goals to become a
neuroscientist and launched her on what would become her lifelong academic path.[1]
Marder received her B.A. from
Brandeis University in 1969[1] and subsequently completed Ph.D. studies at
University of California, San Diego. It was during her time as a graduate student at
UCSD that Marder would be introduced to the specific neural network, the lobster stomatogastric-ganglion system, that would prove pivotal for the rest of her academic career.[1] Marder's doctoral work on the role of
acetylcholine in the lobster STG led to a single-author paper in Nature.[2] She completed her postdoctoral training at the
University of Oregon in
Eugene and the
École Normale Supérieure in
Paris, France. Marder subsequently began her independent research career at
Brandeis University in 1978 as a faculty member in the department in Biology.
Her work on the 30
neurons that compose the lobster stomatogastric ganglion (STG) produced many notable findings. She found that circuits can be modulated by many
neuromodulators, which act on the level of populations of neurons, unlike some neurotransmitters, which can only affect specific target neurons. She pioneered work on
plasticity and
homeostasis, revealing more about how the brain can change dramatically during learning and development yet remain structurally stable. Her recent work examining network variability among healthy individuals shows that a variety of network parameters can produce the same behavioral outcome, challenging a long-standing goal in theoretical neuroscience to model 'ideal' neurons and neural circuits.[3]
Along with
Larry Abbott, she also developed the dynamic clamp method, which enables an experimenter to induce mathematically modeled conductances into living neurons to view the output of theoretical circuits.[4]
Sharp, A. A.; O'Neil, M. B.; Abbott, L. F.; Marder, E. (March 1, 1993). "Dynamic clamp: computer-generated conductances in real neurons". Journal of Neurophysiology. 69 (3): 992–995.
doi:
10.1152/jn.1993.69.3.992.
ISSN0022-3077.
PMID8463821.
Marder, Eve; Goaillard, Jean-Marc; Schulz, David J. (2006). "Variable channel expression in identified single and electrically coupled neurons in different animals". Nature Neuroscience. 9 (3): 356–362.
doi:
10.1038/nn1639.
ISSN1546-1726.
PMID16444270.
S2CID19657439.