Our research seeks to understand how important cognitive behaviors such as learning, remembering, and deciding emerge from the cooperative activity of multi-scale neural processes in intact brains and how those mechanisms are affected by disease.

The Rajan Lab bridges brain research and artificial intelligence by developing integrative theories to describe the mechanisms by which cognitive behaviors emerge from underlying neural processes in the brain. Our integrative theories are based on: (a) neural network models flexible enough to accommodate sufficient levels of biological detail at the neuronal, synaptic, circuit, and multi-region levels, and (b) new and existing mathematical or computational frameworks designed to extract essential mechanistic features in data from well-designed imaging, electrophysiology, and behavioral experiments. These theories currently enable, and will continue to facilitate, critical insights into the learning and execution of cognitive actions, ranging from working memory to high-level phenomena such as reasoning and intuition. Within this broad theme, we are seeking answers to two key questions in neuroscience:

How do animals and humans perform a huge range of complex tasks and behaviors, often by activating the same neural circuits in different ways to perform computations on numerous, hard-to-quantify, abstract cognitive variables?

How do our brains acquire these tasks from few examples, solve unstructured problems, and learn through minimal or no supervision? This is called generalized learning; it is not clear how the brain achieves generalized learning.

To allow for the impressive task repertoire and the ability to achieve generalized learning, the brain areas involved have to be flexible and adaptive to different extents. Consequently, they are quite rich and complex, in terms of the activity patterns or population dynamics they produce, and the underlying circuit and biophysical motifs responsible for them. As such, the signals that are collected through current transformative experimental technologies are also necessarily complex, i.e., intermixed across all different spatial and temporal scales. Principled theory is crucial to make sense of these data, to guide new experimental design, and to address knowledge gaps such as what signals the data contain, over what time scales these signals vary, where signals originate, and how all of these things change in disease contexts.

Theory is crucial on two simultaneous fronts: one, an independent field of research involving subjects such as neural network theory and artificial intelligence/machine learning, among others, and two, a collaborative front involving experimental data analysis to be performed in tight conjunction with these computational models and theories.