For a variety of historical reasons, research in neuroscience has long reflected combined experimental and theoretical approaches. For example, the Hodgkin-Huxley formulation of the action potential (Hodgkin and Huxley, 1952) remains as useful today as it was revolutionary at the time. At the same time, research in neuroscience has long been a source of insight for technological innovation. For example, the discovery that lateral inhibition in the Limulus eye resulted in contrast enhancement of visual images (Hartline and Ratliff, 1957, 1958) provided early information that aided in the development of computational algorithms for contrast enhancement. Likewise, many investigators interested in robotics have been inspired by the organization of insect and other invertebrate nervous systems and skeletal-muscular adaptations to locomotion (Chiel and Beer, 1997; Ayers and Witting, 2007).

Today, neuroscience remains a field in which the interaction between theory and experimental work is rich. A large number of physicists and mathematicians have been drawn into computational neuroscience over the past 20 years, motivated by the sense that the brain poses one of the biggest mysteries left to solve and by their appreciation that understanding of computations in the brain can benefit from quantitative analyses and model building (Dayan and Abbott, 2001). Recognition of the deep evolutionary roots of sensory pathways provides opportunities for collaborative theoretical and experimental research combining neuroscience, microbiology, and plant and animal physiology.

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