Assistant Professor, Department of Psychological and Brain Sciences
Selection is a singular and categorical event. One among numerous alternatives is chosen, while all the others are discarded (equivalently, each alternative can either be either chosen or not). Any neural hypothesis about selection must, therefore, involve competition among the representations of multiple alternatives, and must involve mechanisms that yield the categorical outcome of one alternative being chosen at the expense of all others. These mechanisms must operate in real time, changing with the changing stimuli in the environment. They must be amenable to plasticity, as the needs of an animal change over time. A big research thrust in the lab is to understand how the brain, at the level of neural circuits, accomplishes selection. Because selection is integral to many complex cognitive functions such as attention, decision-making, and perception, the study of the neural mechanisms of selection in different contexts has fundamental implications to the understanding of circuit substrates of psychiatric disorders such as ADHD, autism, and schizophrenia, and has the potential to inform the development of therapeutic strategies.
A parallel research thrust in the lab is to investigate fundamental issues in the design of neural circuits. Considering the evolutionary richness in the animal world investigating how different species implement neural solutions to aspects of behavior that are common across species and critical for survival, such as attention, reward learning, spatial navigation, etc is an extremely valuable endeavor. Such study has the potential to yield deep insights into basic brain function, and into the human condition. To complement current knowledge in the neural bases of these behaviors, the bulk of which comes from studies in mammals, we study these questions in birds.
Finally (and on a different note), a metaphor that is commonly used for thinking about the brain is that it is a biological computer. However, there are fundamental differences between how a brain is designed and constructed, and how a computer is designed and put together, thereby limiting the extent to which this metaphor works. Importantly, brains can perform "easily" certain computational tasks that are considered to be hard for traditional computers. Could the differences in how brains and computers are built account for these differences in performance? If so, can we draw insights from studying brains and use them to build more efficient, versatile and powerful computing machines?