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Bright Purple

Computation + Machine Intelligence

We find rules of how the mind works hidden in brain data

Center for Neurocomputation and Machine Intelligence

One of three research pillars at the Wu Tsai Institute, the Center for Neurocomputation and Machine Intelligence advances understanding of human cognition by integrating data science, computer science, applied math, and engineering into the analysis and modeling of neuroscience data. The mathematical, statistical, and computational tools of these fields provide a common language to bridge the many branches of neuroscience and derive abstract principles of cognition. In turn, these theories generate hypotheses about brain mechanisms that can be tested experimentally and incorporated into artificial systems.

John Lafferty

Advances in computational fields typically permeate experimental disciplines very gradually, often after they are no longer on the leading edge. The Center is closing this loop so that the latest algorithms and technologies can immediately impact the design, analysis, and interpretation of biological and psychological studies.

John Lafferty, PhD, Director of the Center for Neurocomputation and Machine Intelligence and John C. Malone Professor of Statistics and Data Science

We develop next-generation computational frameworks for neuroscience, identifying abstractions and creating models that help uncover principles of cognition

We deploy emerging artificial intelligence, machine learning, and data science methodologies for integrating neuroscience data between scales, modalities, and species

We work together across traditional boundaries to develop a common language by lowering barriers to collaboration between theoretical and empirical approaches

The Center for Neurocomputation and Machine Intelligence contributes to the collective mission of the Wu Tsai Institute by developing and deploying new data analysis tools to make sense of the large and noisy neuroscience datasets collected by the Institute’s other Centers and by researchers in the field. These vast datasets inform the creation of new computational models of cognition. Completing the cycle, these new models offer predictions and interpretations about the brain and behavior to be evaluated by the other Centers.

Center Resources

The WTI Experimental Computing and Visualization Core at 100 College Street provides a home for building and testing experimental computer architectures for neuroscience devices and applications, as well as display technologies for visualizing and interacting with data.

The WTI High-Performance Compute and Storage Cluster housed off-site on West Campus supplies access to enterprise-grade CPU and GPU nodes along with high-speed storage to empower computationally intensive data analysis and modeling.

The Center’s staff provides computational support services, working with researchers to help them learn about and incorporate high-performance computing techniques into their projects. 

The Center’s hardware will be wrapped with a software informatics layer that facilitates the curation and sharing of neuroscience data of all types with the broader community to enable collaboration and integration. Learn more about the Center’s resources.