Jack Gallant, PhD

Professor of Psychology


Affiliated with the Department of Electrical Engineering and Computer Science. Also affiliated with the graduate programs in Bioengineering, Biophysics, Neuroscience and Vision Science.

Research Areas

The mammalian cerebral cortex is a 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 comes 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.

Once the data are collected, we use classical statistical tools, Bayesian analysis and machine learning approaches to fit computational models to the brain data. (These are usually called “encoding models”, because they describe how information about the world is encoded in the brain.) We usually evaluate several different, competing models in order to find the one model that most accurately predicts responses, using a separate data set that was not used to estimate the model. The resulting encoding models describe what specific information is represented explicitly at each location across the cerebral cortex.

Computational encoding models that accurately predict brain activity are the gold standard of systems and cognitive neuroscience. However, these models also have many practical uses. They provide a new tool for neurological evaluation and diagnosis. They also provide a critical foundation for developing therapies to repair brain damage (after all, one needs to understand how a system functions before one can hope to repair it). Finally, computational encoding models can be inverted by means of a Bayesian framework, in order to decode brain activity. This provides a direct and principled way to do “brain decoding”, and to build brain-machine interfaces (BMI) and neural prosthetics.