At LIMLab, we investigate the neural principles underlying how the nervous system computes, represents, and integrates sensory memories and priors to learn and infer meaningful statistical patterns and abstract relationships in the environment.
Our approach combines high-throughput training with sophisticated, well-controlled behavioral paradigms, alongside powerful tools for monitoring and manipulating neural circuits. In all our research programs, experiments are tightly integrated with hypotheses derived from theoretical investigations.
Leveraging expertise in theoretical neuroscience, neural network dynamics, rodent cognition, behavioral modeling, imaging, electrophysiology, and optogenetics, we aim to bridge the gap between behavioral-level understanding of memory and statistical learning and its circuit- and systems-level implementation.
