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Variability, Homeostasis per Contents and Compensation in Rhythmic Motor Networks

Eve Marder, Brandeis University


DR. MARDER: I’m a neuroscientist. I should say that I’m an experimental neuroscientist who has worked at various times and places with a variety of theorists, including my fellow speaker, Nancy Kopell. What I would like to do today is give you a sense of some problems and approaches where neuroscience and network science intersect. I realize that you’ll think some of my take home messages are probably obvious, even though my neuroscience colleagues often get very disturbed and distressed and start foaming at the mouth.

The problem that we have been working on for the last 10 or 15 years, like all good problems, is both completely obvious and not. For this group it’s basically the problem of how well-tuned do the parameters that underlie brain networks have to be for them to function correctly? If you were to have asked most experimental neuroscientists 20 years ago how well-tuned any given synapse would have to be, or how well-tuned the number of any kind of channel in the cell would have to be, they would have probably said 5 percent plus or minus. You should know that neuroscientists have traditionally had the perspective that things have to be very tightly regulated, and we can talk about why that is in the future.

About 10 or 15 years ago we started working on a problem that has since been renamed homeostasis and compensation. This comes back to something that is true in all biological systems, and not true in mechanical or engineering systems in the same way: almost all the neurons in your brain will have lived for your entire life, but every single membrane protein that gives rise to the ability of cells to signal is being replaced on a time scale of minutes, hours, days or weeks. The most long-lasting ones are sitting in the membrane for maybe a couple of weeks, which means that every single neuron and every single synapse is constantly rebuilding itself. This in turn means that you are faced with an incredible engineering task, which is how do you maintain stable brain function? I still remember how to name a tree and a daffodil and all the things I learned as a child, despite the fact that my brain is constantly, in the microstructure, rebuilding itself. When we started thinking about this, and this goes back a number of years, Larry Abbott and I worked on a series of models that basically used very simple negative feedback homeostatic mechanisms to try and understand how you could get stable neuronal function despite turnover and perturbations. What that led to was going back to step one and saying, how well do negative-feedback self-tuning stability mechanisms have to work? You have



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