Abstract: Series 112, Lecture 2
The Harvey Lectures Series 112 (2016—2017)
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Lecture #2: Thursday, November 17, 2016 — Watch Video of Lecture
Variability and Robustness in Neurons and Networks
Eve Marder, PhD
Professor of Biology
Brandeis University
Waltham, Massachusetts
Long-lived animals, such as lobsters and humans, confront a complex problem: their neurons live for many, many years, yet all of the molecules that are important for neuronal and synaptic function are being replaced on time-scales from minutes to weeks. How do nervous systems maintain stable function in the face of molecular turnover? And, does stable circuit function demand constancy in the cellular and molecular components that give rise to brain functions? This leads to the question of how well-tuned do brains need to be to produce behavior that we consider healthy and normal? Experimental work on the crustacean stomatogastric ganglion (STG) has revealed a 2-6 fold variability in many of the parameters that are important for circuit dynamics.
At the same time, a body of theoretical work shows that similar network performance can arise from diverse underlying parameter sets. Together, these lines of evidence suggest that each individual animal, at any moment in its life-time, has found a different solution to producing “good enough” motor patterns for healthy performance in the world. This poses the question of the extent to which animals with different sets of underlying circuit parameters can respond reliably and robustly to environmental perturbations and neuromodulation. Consequently, we study the effects of temperature, pH and neuromodulation on the pyloric rhythm of crabs. While all animals respond remarkably well to large environmental perturbations, extreme perturbations that produce system “crashes” reveal the underlying parameter differences in the population. Moreover, new models of homeostatic regulation of intrinsic excitability give insight into the kinds of mechanisms that could give rise to the highly variable solutions to stable circuit performance.