This project aims to understand how large populations of neurons transform their encoded information to drive behaviors meaningful to the organism. This will be accomplished in two ways. First, the research will derive new analysis methods that experimentalists can use to interpret neural data from naturalistic tasks of moderate complexity. Second, by the project will create a broadly applicable computational framework for synthesizing these analyses into a theory of probabilistic neural computation. Both of these components are informed by three basic principles: information in the brain is distributed across many neurons, sensory evidence is weighted by its reliability, and neural computation occurs in multiple stages. Current analyses that connect animal behavior to neural activities apply to tasks that are so simple that an animal would not actually need a brain to solve them: the same computations could be accomplished in a single step by wiring the sensory organs directly to the muscles. Clearly there is a need to study more complex tasks that require multi-step computations, and the proposed research will provide the rigorous statistical foundation needed to analyze data from such studies. The research will also have a broader educational impact by creating interactive teaching games that explain concepts needed for thinking about big neuroscience data.

The long-term goal of this research program is to explain brain function by constructing quantitative theories of how distributed nonlinear neural computation implements principles of statistical reasoning. To accomplish this goal, this project will create a normative theory for what information about naturalistic tasks should be encoded in neural populations, and data analyses that can reveal which aspects of that information are actually decoded. The normative theory is based on probabilistic population codes, a model in which large-scale neural activity patterns encode not just estimates of a stimulus, but also the reliability of those estimates. This model is currently applied only to small-scale inference problems, and one aim of this project is to extend this model by constructing biologically plausible network models for complex naturalistic tasks involving many interacting variables. The key components of this model, and indeed of any model of naturalistic computation, are nonlinear operations. To determine whether the posited nonlinear computations occur in a real brain, the other aim of the project is to derive a statistical analysis technique centered on a novel generalization of standard choice-related activity, termed nonlinear choice correlation. By combining this measure with estimates of neural correlations, experimentalists will be able to infer the class of distributed nonlinear computations the brain uses from simultaneous recording of neural activity and animal behavior.

National Science Foundation (NSF)
Division of Integrative Organismal Systems (IOS)
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Sridhar Raghavachari
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Baylor College of Medicine
United States
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