Speakers > Eve Marder
Eve Marder is the Victor and Gwendolyn Beinfield Professor of Neuroscience at Brandeis University. Eve Marder has been working on the stomatogastric nervous system of crustaceans for more than 40 years, demonstrating that invertebrate models can help us elucidate fundamental principles of neural circuit function that are difficult to tackle in larger mammalian networks. Amongst her many contributions, she demonstrated how networks can be reconfigured by neuromodulators, how homeostatic plasticity underlies the recovery of neuronal activity after perturbations, how degenerate the biophysical solutions underlying neuronal function are and how resilient neural networks are to environmental changes. She is a former president of the Amercian Society for Neuroscience, a member of the National Academy of Sciences and has received many prestigious awards such as the Gruber Neuroscience Prize or the Kavli Prize in Neuroscience. From Modulation of Small Degenerate Circuits to Climate Change The crustacean stomatogastric nervous system houses two important central pattern generating circuits that generate the fast pyloric rhythm and the slower gastric mill rhythm. Numerous experimental and computational studies have demonstrated that individual neurons and small circuits are degenerate, that is, different sets of underlying intrinsic and synaptic currents can produce very similar motor patterns. This raises the question of whether these degenerate solutions can respond robustly and reliably to perturbations. Consequently, we have been studying a number of global perturbations, including temperature, pH, and high extracellular potassium concentrations. While both the pyloric and gastric mill rhythms can operate over a range of temperatures, analysis of data collected over many years shows that ocean temperatures are correlated with the range over which these rhythms can function reliably. Moreover, many long-term perturbations produce "cryptic" changes that are not visible in the absence of perturbation, but are only revealed when the systems are challenged. These data give potential insight into how prior history can produce hidden changes in circuit function that change the reliance of circuits to future perturbations.
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