Systems and computational neuroscience involve the study of information processing within circuits of neurons in the brain using experimental and computational techniques.

Researchers in the system neuroscience area can experimentally verify well-defined questions about neural function. Studying neural activity in alert animals, while an intact nervous system is producing intelligent behavior, yields particularly significant insights. Researchers can uncover correlations between intelligent behavior and corresponding neuronal activation, map the functional architecture of the brain, and reconstruct the flow of information within it.

Systems neuroscientists are making advances in the study of higher cognitive functions. In the case of visual perception, for example, investigators have discovered neurons in the brain whose electrical activation correlates with the most complex visual tasks, such as recognition and categorization. Investigators can now ask specifically where and how signals underlying various higher cognitive processes arise within the brain. They can study several aspects of intelligence, such as movement planning, spatial navigation, object recognition, imitation and emotion; they can also elucidate how these cognitive abilities are learned from experience.

The ultimate goal is to understand how intelligence emerges from the dynamic interactions between individual cells and larger neural circuits that give rise to the patterns of electrical activity associated with higher brain function. Computational neuroscience provides tools, under the form of quantitative models, to summarize the increasing wealth of complex physiological data, interpret and analyze them, and plan new experiments.

At the McGovern Institute, the combination of systems and computational neuroscience will represent an integrative focus for framing well-defined questions about higher neural functions and for experimental investigation of these questions.