- Cognitive Neuroscience
- Computational modeling
- Functional MRI
Professor of Psychology
University of California, Berkeley
The mammalian cerebral cortex is multi-scale biological computing device consisting of billions of neurons, arranged in layered, local circuits. These local circuits are arranged into columns, and groups of columns form an area. Connections between neurons within a local circuit, column and area are both convergent (projections within structures) and divergent (projections between structures). Both feed-forward and feed-back connections are typical, and information may flow over multiple routes to reach the same target. The result is a hierarchical, parallel, highly interconnected network of areas that tile the cerebral cortex.
The information that is represented in each cortical area reflects a nonlinear sum of the information represented in all areas that provide feed-forward or feed-back input to the area. Thus, each area represents information explicitly that is only implicit in the input. This property suggests that it should be possible to understand brain computation by first discovering cortical areas, and then determining what specific information is mapped across each area. The central goal of our research program is to discover how the mammalian brain represents information about the world and about its own mental states, by identifying and characterizing these cortical maps.
To address this problem, our laboratory makes heavy use of an inductive scientific approach called system identification. System identification is a systematic approach for discovering the computational principles of an unknown system such as the brain. Most of the data collected in our lab came from functional magnetic resonance imaging (fMRI) studies of the human brain. In a typical experiment these data are collected under very general conditions: while subjects watch movies, listen to natural sounds and so on. When necessary, we supplement this approach with targeted experiments that are optimized to test very specific hypotheses about brain function.