The human brain has evolved over millions of years to become a vast network of billions of neurons and synaptic connections. Understanding it is one of humankind's greatest pursuits.
But to understand how the brain processes information, researchers must first understand the very basics of neurons even down to how proteins inside the neurons act to change the neuron's voltage.
To do so requires a balance of experimentation and computer modeling a partnership across disciplines traversed by Bill Kath, professor of engineering sciences and applied mathematics in the McCormick School of Engineering and Applied Science, and Nelson Spruston, professor of neurobiology and physiology in the Weinberg College of Arts and Sciences.
The two have worked together for more than a decade, with Spruston designing experiments and Kath developing computer models that explain the results that Spruston found. (It also works the other way: Kath's models have provided Spruston with ideas to test experimentally.)
Spruston has been studying ion channels of neurons that change their shape when activated, allowing sodium to enter from outside the neuron. This changes the voltage of the neuron, causing the neuron to fire and send off a chain of neural activity within the brain. The difficulty in modeling such behavior lies in the time scale over which this happens anywhere from fractions of a millisecond out to several seconds.
So the two, along with graduate student Vilas Menon, took a cue from nature and used the process of evolution to study one of evolution's greatest achievements.
Evolutionary algorithms work like this: rather than making one model, researchers make 100 models with many different parameters. They then run those models (using high-speed computers) and compare the results to the experimental data to see how well they match. Researchers then keep the best traits of different models and mix and match (breeding) t
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